ARIMAX.ipynb

{"cells":[{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737331988,"execution_millis":8299,"deepnote_to_be_reexecuted":false,"cell_id":"fc30f33246fe4c8c99e70a736036bdae","deepnote_cell_type":"code"},"source":"import pandas as pd\nimport numpy as np\n!pip install statsmodels\n\nimport statsmodels.api as sm\nfrom matplotlib import pyplot as plt\nfrom statsmodels.tsa.stattools import adfuller\nfrom statsmodels.tsa.seasonal import seasonal_decompose\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\nfrom statsmodels.tsa.arima_model import ARIMA\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\nfrom pandas.plotting import register_matplotlib_converters\nregister_matplotlib_converters()","block_group":"fc30f33246fe4c8c99e70a736036bdae","execution_count":null,"outputs":[{"name":"stdout","text":"Requirement already satisfied: statsmodels in /root/venv/lib/python3.9/site-packages (0.14.0)\nRequirement already satisfied: pandas>=1.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from statsmodels) (1.2.5)\nRequirement already satisfied: patsy>=0.5.2 in /root/venv/lib/python3.9/site-packages (from statsmodels) (0.5.4)\nRequirement already satisfied: numpy>=1.18 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from statsmodels) (1.23.4)\nRequirement already satisfied: scipy!=1.9.2,>=1.4 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from statsmodels) (1.9.3)\nRequirement already satisfied: packaging>=21.3 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from statsmodels) (21.3)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from packaging>=21.3->statsmodels) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7.3 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from pandas>=1.0->statsmodels) (2.8.2)\nRequirement already satisfied: pytz>=2017.3 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pandas>=1.0->statsmodels) (2022.5)\nRequirement already satisfied: six in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"35b549a1316645449cb4a95e988bfc07","deepnote_cell_type":"text-cell-h2"},"source":"## Data Import (*fruit only)","block_group":"c6e5f28c09a546ed809ecb4cac16b82b"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737340269,"execution_millis":462,"deepnote_to_be_reexecuted":false,"cell_id":"d289096f5bb2466a83f30822b98b7d3d","deepnote_cell_type":"code"},"source":"fruit2 = pd.read_csv(\"Raw Data/fruit_2.csv\")\nfruit2 = fruit2[[\"year\", \"commodity\", \"loss_percentage\"]].groupby([\"year\", \"commodity\"]).min().reset_index()\nfruit2 = fruit2[fruit2[\"year\"] >= 2000]\nfruit2.head()","block_group":"c8e12a35d5c747f39d8d025ef2739fab","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":2,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":3,"row_count":5,"columns":[{"name":"year","dtype":"int64","stats":{"unique_count":1,"nan_count":0,"min":"2000","max":"2000","histogram":[{"bin_start":1999.5,"bin_end":1999.6,"count":0},{"bin_start":1999.6,"bin_end":1999.7,"count":0},{"bin_start":1999.7,"bin_end":1999.8,"count":0},{"bin_start":1999.8,"bin_end":1999.9,"count":0},{"bin_start":1999.9,"bin_end":2000,"count":0},{"bin_start":2000,"bin_end":2000.1,"count":5},{"bin_start":2000.1,"bin_end":2000.2,"count":0},{"bin_start":2000.2,"bin_end":2000.3,"count":0},{"bin_start":2000.3,"bin_end":2000.4,"count":0},{"bin_start":2000.4,"bin_end":2000.5,"count":0}]}},{"name":"commodity","dtype":"object","stats":{"unique_count":5,"nan_count":0,"categories":[{"name":"Apple 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juice","loss_percentage":26.6667,"_deepnote_index_column":350},{"year":2000,"commodity":"Apples","loss_percentage":4,"_deepnote_index_column":351},{"year":2000,"commodity":"Apricots","loss_percentage":9,"_deepnote_index_column":352},{"year":2000,"commodity":"Blueberries","loss_percentage":8,"_deepnote_index_column":353},{"year":2000,"commodity":"Cantaloupes and other melons","loss_percentage":8,"_deepnote_index_column":354}]},"text/plain":"     year                     commodity  loss_percentage\n350  2000                   Apple juice          26.6667\n351  2000                        Apples           4.0000\n352  2000                      Apricots           9.0000\n353  2000                   Blueberries           8.0000\n354  2000  Cantaloupes and other melons           8.0000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>commodity</th>\n      <th>loss_percentage</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>350</th>\n      <td>2000</td>\n      <td>Apple juice</td>\n      <td>26.6667</td>\n    </tr>\n    <tr>\n      <th>351</th>\n      <td>2000</td>\n      <td>Apples</td>\n      <td>4.0000</td>\n    </tr>\n    <tr>\n      <th>352</th>\n      <td>2000</td>\n      <td>Apricots</td>\n      <td>9.0000</td>\n    </tr>\n    <tr>\n      <th>353</th>\n      <td>2000</td>\n      <td>Blueberries</td>\n      <td>8.0000</td>\n    </tr>\n    <tr>\n      <th>354</th>\n      <td>2000</td>\n      <td>Cantaloupes and other melons</td>\n      <td>8.0000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737340674,"execution_millis":357,"deepnote_to_be_reexecuted":false,"cell_id":"63a2359b33e14768b15254dec218a84a","deepnote_cell_type":"code"},"source":"foodYield = pd.read_csv(\"Raw Data/FoodYield.csv\")\nfoodYield.head()","block_group":"0adf2f8a21ab4685a22b2b0970673f80","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":15,"row_count":5,"columns":[{"name":"Domain Code","dtype":"object","stats":{"unique_count":1,"nan_count":0,"categories":[{"name":"QCL","count":5}]}},{"name":"Domain","dtype":"object","stats":{"unique_count":1,"nan_count":0,"categories":[{"name":"Crops and livestock products","count":5}]}},{"name":"Area Code (M49)","dtype":"int64","stats":{"unique_count":1,"nan_count":0,"min":"840","max":"840","histogram":[{"bin_start":839.5,"bin_end":839.6,"count":0},{"bin_start":839.6,"bin_end":839.7,"count":0},{"bin_start":839.7,"bin_end":839.8,"count":0},{"bin_start":839.8,"bin_end":839.9,"count":0},{"bin_start":839.9,"bin_end":840,"count":0},{"bin_start":840,"bin_end":840.1,"count":5},{"bin_start":840.1,"bin_end":840.2,"count":0},{"bin_start":840.2,"bin_end":840.3,"count":0},{"bin_start":840.3,"bin_end":840.4,"count":0},{"bin_start":840.4,"bin_end":840.5,"count":0}]}},{"name":"Area","dtype":"object","stats":{"unique_count":1,"nan_count":0,"categories":[{"name":"United States of America","count":5}]}},{"name":"Element 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(CPC)","dtype":"float64","stats":{"unique_count":1,"nan_count":0,"min":"1371.0","max":"1371.0","histogram":[{"bin_start":1370.5,"bin_end":1370.6,"count":0},{"bin_start":1370.6,"bin_end":1370.7,"count":0},{"bin_start":1370.7,"bin_end":1370.8,"count":0},{"bin_start":1370.8,"bin_end":1370.9,"count":0},{"bin_start":1370.9,"bin_end":1371,"count":0},{"bin_start":1371,"bin_end":1371.1,"count":5},{"bin_start":1371.1,"bin_end":1371.2,"count":0},{"bin_start":1371.2,"bin_end":1371.3,"count":0},{"bin_start":1371.3,"bin_end":1371.4,"count":0},{"bin_start":1371.4,"bin_end":1371.5,"count":0}]}},{"name":"Item","dtype":"object","stats":{"unique_count":1,"nan_count":0,"categories":[{"name":"Almonds, in shell","count":5}]}},{"name":"Year 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g/ha","count":5}]}},{"name":"Value","dtype":"int64","stats":{"unique_count":5,"nan_count":0,"min":"26342","max":"36275","histogram":[{"bin_start":26342,"bin_end":27335.3,"count":1},{"bin_start":27335.3,"bin_end":28328.6,"count":0},{"bin_start":28328.6,"bin_end":29321.9,"count":1},{"bin_start":29321.9,"bin_end":30315.2,"count":0},{"bin_start":30315.2,"bin_end":31308.5,"count":0},{"bin_start":31308.5,"bin_end":32301.8,"count":0},{"bin_start":32301.8,"bin_end":33295.1,"count":0},{"bin_start":33295.1,"bin_end":34288.4,"count":1},{"bin_start":34288.4,"bin_end":35281.7,"count":0},{"bin_start":35281.7,"bin_end":36275,"count":2}]}},{"name":"Flag","dtype":"object","stats":{"unique_count":1,"nan_count":0,"categories":[{"name":"A","count":5}]}},{"name":"Flag Description","dtype":"object","stats":{"unique_count":1,"nan_count":0,"categories":[{"name":"Official 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Description":"Official figure","Note":"nan","_deepnote_index_column":0},{"Domain Code":"QCL","Domain":"Crops and livestock products","Area Code (M49)":840,"Area":"United States of America","Element Code":5419,"Element":"Yield","Item Code (CPC)":1371,"Item":"Almonds, in shell","Year Code":2001,"Year":2001,"Unit":"100 g/ha","Value":28402,"Flag":"A","Flag Description":"Official figure","Note":"nan","_deepnote_index_column":1},{"Domain Code":"QCL","Domain":"Crops and livestock products","Area Code (M49)":840,"Area":"United States of America","Element Code":5419,"Element":"Yield","Item Code (CPC)":1371,"Item":"Almonds, in shell","Year Code":2002,"Year":2002,"Unit":"100 g/ha","Value":36275,"Flag":"A","Flag Description":"Official figure","Note":"nan","_deepnote_index_column":2},{"Domain Code":"QCL","Domain":"Crops and livestock products","Area Code (M49)":840,"Area":"United States of America","Element Code":5419,"Element":"Yield","Item Code (CPC)":1371,"Item":"Almonds, in shell","Year Code":2003,"Year":2003,"Unit":"100 g/ha","Value":35325,"Flag":"A","Flag Description":"Official figure","Note":"nan","_deepnote_index_column":3},{"Domain Code":"QCL","Domain":"Crops and livestock products","Area Code (M49)":840,"Area":"United States of America","Element Code":5419,"Element":"Yield","Item Code (CPC)":1371,"Item":"Almonds, in shell","Year Code":2004,"Year":2004,"Unit":"100 g/ha","Value":34074,"Flag":"A","Flag Description":"Official figure","Note":"nan","_deepnote_index_column":4}]},"text/plain":"  Domain Code                        Domain  Area Code (M49)  \\\n0         QCL  Crops and livestock products              840   \n1         QCL  Crops and livestock products              840   \n2         QCL  Crops and livestock products              840   \n3         QCL  Crops and livestock products              840   \n4         QCL  Crops and livestock products              840   \n\n                       Area  Element Code Element  Item Code (CPC)  \\\n0  United States of America          5419   Yield           1371.0   \n1  United States of America          5419   Yield           1371.0   \n2  United States of America          5419   Yield           1371.0   \n3  United States of America          5419   Yield           1371.0   \n4  United States of America          5419   Yield           1371.0   \n\n                Item  Year Code  Year      Unit  Value Flag Flag Description  \\\n0  Almonds, in shell       2000  2000  100 g/ha  26342    A  Official figure   \n1  Almonds, in shell       2001  2001  100 g/ha  28402    A  Official figure   \n2  Almonds, in shell       2002  2002  100 g/ha  36275    A  Official figure   \n3  Almonds, in shell       2003  2003  100 g/ha  35325    A  Official figure   \n4  Almonds, in shell       2004  2004  100 g/ha  34074    A  Official figure   \n\n   Note  \n0   NaN  \n1   NaN  \n2   NaN  \n3   NaN  \n4   NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Domain Code</th>\n      <th>Domain</th>\n      <th>Area Code (M49)</th>\n      <th>Area</th>\n      <th>Element Code</th>\n      <th>Element</th>\n      <th>Item Code (CPC)</th>\n      <th>Item</th>\n      <th>Year Code</th>\n      <th>Year</th>\n      <th>Unit</th>\n      <th>Value</th>\n      <th>Flag</th>\n      <th>Flag Description</th>\n      <th>Note</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>QCL</td>\n      <td>Crops and livestock products</td>\n      <td>840</td>\n      <td>United States of America</td>\n      <td>5419</td>\n      <td>Yield</td>\n      <td>1371.0</td>\n      <td>Almonds, in shell</td>\n      <td>2000</td>\n      <td>2000</td>\n      <td>100 g/ha</td>\n      <td>26342</td>\n      <td>A</td>\n      <td>Official figure</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>QCL</td>\n      <td>Crops and livestock products</td>\n      <td>840</td>\n      <td>United States of America</td>\n      <td>5419</td>\n      <td>Yield</td>\n      <td>1371.0</td>\n      <td>Almonds, in shell</td>\n      <td>2001</td>\n      <td>2001</td>\n      <td>100 g/ha</td>\n      <td>28402</td>\n      <td>A</td>\n      <td>Official figure</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>QCL</td>\n      <td>Crops and livestock products</td>\n      <td>840</td>\n      <td>United States of America</td>\n      <td>5419</td>\n      <td>Yield</td>\n      <td>1371.0</td>\n      <td>Almonds, in shell</td>\n      <td>2002</td>\n      <td>2002</td>\n      <td>100 g/ha</td>\n      <td>36275</td>\n      <td>A</td>\n      <td>Official figure</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>QCL</td>\n      <td>Crops and livestock products</td>\n      <td>840</td>\n      <td>United States of America</td>\n      <td>5419</td>\n      <td>Yield</td>\n      <td>1371.0</td>\n      <td>Almonds, in shell</td>\n      <td>2003</td>\n      <td>2003</td>\n      <td>100 g/ha</td>\n      <td>35325</td>\n      <td>A</td>\n      <td>Official figure</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>QCL</td>\n      <td>Crops and livestock products</td>\n      <td>840</td>\n      <td>United States of America</td>\n      <td>5419</td>\n      <td>Yield</td>\n      <td>1371.0</td>\n      <td>Almonds, in shell</td>\n      <td>2004</td>\n      <td>2004</td>\n      <td>100 g/ha</td>\n      <td>34074</td>\n      <td>A</td>\n      <td>Official figure</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737340996,"execution_millis":255,"deepnote_to_be_reexecuted":false,"cell_id":"f0daa77443da420f848b98179a63242e","deepnote_cell_type":"code"},"source":"foodYield = foodYield.rename(columns={\"Value\":\"crop_yield\"})\nfoodYield = foodYield[[\"Year\", \"Item\", \"crop_yield\"]]\nfruits_am = pd.merge(fruit2, foodYield, how='left', left_on=['year', 'commodity'], right_on=['Year', 'Item'])\nfruits_am = fruits_am[[\"year\", \"commodity\", \"loss_percentage\", \"crop_yield\"]].dropna()\nfruits_am.head()","block_group":"1a363b944e6f4b62b4551957e555b12f","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":4,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":4,"row_count":5,"columns":[{"name":"year","dtype":"int64","stats":{"unique_count":1,"nan_count":0,"min":"2000","max":"2000","histogram":[{"bin_start":1999.5,"bin_end":1999.6,"count":0},{"bin_start":1999.6,"bin_end":1999.7,"count":0},{"bin_start":1999.7,"bin_end":1999.8,"count":0},{"bin_start":1999.8,"bin_end":1999.9,"count":0},{"bin_start":1999.9,"bin_end":2000,"count":0},{"bin_start":2000,"bin_end":2000.1,"count":5},{"bin_start":2000.1,"bin_end":2000.2,"count":0},{"bin_start":2000.2,"bin_end":2000.3,"count":0},{"bin_start":2000.3,"bin_end":2000.4,"count":0},{"bin_start":2000.4,"bin_end":2000.5,"count":0}]}},{"name":"commodity","dtype":"object","stats":{"unique_count":5,"nan_count":0,"categories":[{"name":"Apples","count":1},{"name":"Apricots","count":1},{"name":"3 others","count":3}]}},{"name":"loss_percentage","dtype":"float64","stats":{"unique_count":3,"nan_count":0,"min":"4.0","max":"9.0","histogram":[{"bin_start":4,"bin_end":4.5,"count":2},{"bin_start":4.5,"bin_end":5,"count":0},{"bin_start":5,"bin_end":5.5,"count":0},{"bin_start":5.5,"bin_end":6,"count":0},{"bin_start":6,"bin_end":6.5,"count":0},{"bin_start":6.5,"bin_end":7,"count":0},{"bin_start":7,"bin_end":7.5,"count":0},{"bin_start":7.5,"bin_end":8,"count":0},{"bin_start":8,"bin_end":8.5,"count":2},{"bin_start":8.5,"bin_end":9,"count":1}]}},{"name":"crop_yield","dtype":"float64","stats":{"unique_count":5,"nan_count":0,"min":"51253.0","max":"273483.0","histogram":[{"bin_start":51253,"bin_end":73476,"count":1},{"bin_start":73476,"bin_end":95699,"count":0},{"bin_start":95699,"bin_end":117922,"count":1},{"bin_start":117922,"bin_end":140145,"count":0},{"bin_start":140145,"bin_end":162368,"count":0},{"bin_start":162368,"bin_end":184591,"count":1},{"bin_start":184591,"bin_end":206814,"count":0},{"bin_start":206814,"bin_end":229037,"count":0},{"bin_start":229037,"bin_end":251260,"count":1},{"bin_start":251260,"bin_end":273483,"count":1}]}},{"name":"_deepnote_index_column","dtype":"int64"}],"rows":[{"year":2000,"commodity":"Apples","loss_percentage":4,"crop_yield":273483,"_deepnote_index_column":1},{"year":2000,"commodity":"Apricots","loss_percentage":9,"crop_yield":106579,"_deepnote_index_column":2},{"year":2000,"commodity":"Blueberries","loss_percentage":8,"crop_yield":51253,"_deepnote_index_column":3},{"year":2000,"commodity":"Cantaloupes and other melons","loss_percentage":8,"crop_yield":230769,"_deepnote_index_column":4},{"year":2000,"commodity":"Cranberries","loss_percentage":4,"crop_yield":172109,"_deepnote_index_column":5}]},"text/plain":"   year                     commodity  loss_percentage  crop_yield\n1  2000                        Apples              4.0    273483.0\n2  2000                      Apricots              9.0    106579.0\n3  2000                   Blueberries              8.0     51253.0\n4  2000  Cantaloupes and other melons              8.0    230769.0\n5  2000                   Cranberries              4.0    172109.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>commodity</th>\n      <th>loss_percentage</th>\n      <th>crop_yield</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>2000</td>\n      <td>Apples</td>\n      <td>4.0</td>\n      <td>273483.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2000</td>\n      <td>Apricots</td>\n      <td>9.0</td>\n      <td>106579.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2000</td>\n      <td>Blueberries</td>\n      <td>8.0</td>\n      <td>51253.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2000</td>\n      <td>Cantaloupes and other melons</td>\n      <td>8.0</td>\n      <td>230769.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2000</td>\n      <td>Cranberries</td>\n      <td>4.0</td>\n      <td>172109.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737341031,"execution_millis":194,"deepnote_to_be_reexecuted":false,"cell_id":"27026892737b49babffc9fc4e44c4ee4","deepnote_cell_type":"code"},"source":"fruits_am[\"lost_crop_yield\"] = (fruits_am[\"loss_percentage\"]/100) * fruits_am[\"crop_yield\"]\nfruits_am[\"year\"] = pd.to_datetime(fruits_am[\"year\"].astype(str), format='%Y')\nfruits_am.head()","block_group":"2a34382b8a1c459e964272158f00aa17","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":5,"row_count":5,"columns":[{"name":"year","dtype":"datetime64[ns]","stats":{"unique_count":1,"nan_count":0,"min":"2000-01-01 00:00:00","max":"2000-01-01 00:00:00","histogram":null}},{"name":"commodity","dtype":"object","stats":{"unique_count":5,"nan_count":0,"categories":[{"name":"Apples","count":1},{"name":"Apricots","count":1},{"name":"3 others","count":3}]}},{"name":"loss_percentage","dtype":"float64","stats":{"unique_count":3,"nan_count":0,"min":"4.0","max":"9.0","histogram":[{"bin_start":4,"bin_end":4.5,"count":2},{"bin_start":4.5,"bin_end":5,"count":0},{"bin_start":5,"bin_end":5.5,"count":0},{"bin_start":5.5,"bin_end":6,"count":0},{"bin_start":6,"bin_end":6.5,"count":0},{"bin_start":6.5,"bin_end":7,"count":0},{"bin_start":7,"bin_end":7.5,"count":0},{"bin_start":7.5,"bin_end":8,"count":0},{"bin_start":8,"bin_end":8.5,"count":2},{"bin_start":8.5,"bin_end":9,"count":1}]}},{"name":"crop_yield","dtype":"float64","stats":{"unique_count":5,"nan_count":0,"min":"51253.0","max":"273483.0","histogram":[{"bin_start":51253,"bin_end":73476,"count":1},{"bin_start":73476,"bin_end":95699,"count":0},{"bin_start":95699,"bin_end":117922,"count":1},{"bin_start":117922,"bin_end":140145,"count":0},{"bin_start":140145,"bin_end":162368,"count":0},{"bin_start":162368,"bin_end":184591,"count":1},{"bin_start":184591,"bin_end":206814,"count":0},{"bin_start":206814,"bin_end":229037,"count":0},{"bin_start":229037,"bin_end":251260,"count":1},{"bin_start":251260,"bin_end":273483,"count":1}]}},{"name":"lost_crop_yield","dtype":"float64","stats":{"unique_count":5,"nan_count":0,"min":"4100.24","max":"18461.52","histogram":[{"bin_start":4100.24,"bin_end":5536.368,"count":1},{"bin_start":5536.368,"bin_end":6972.496,"count":1},{"bin_start":6972.496,"bin_end":8408.624,"count":0},{"bin_start":8408.624,"bin_end":9844.752,"count":1},{"bin_start":9844.752,"bin_end":11280.880000000001,"count":1},{"bin_start":11280.880000000001,"bin_end":12717.008,"count":0},{"bin_start":12717.008,"bin_end":14153.136,"count":0},{"bin_start":14153.136,"bin_end":15589.264000000001,"count":0},{"bin_start":15589.264000000001,"bin_end":17025.392,"count":0},{"bin_start":17025.392,"bin_end":18461.52,"count":1}]}},{"name":"_deepnote_index_column","dtype":"int64"}],"rows":[{"year":"2000-01-01 00:00:00","commodity":"Apples","loss_percentage":4,"crop_yield":273483,"lost_crop_yield":10939.32,"_deepnote_index_column":1},{"year":"2000-01-01 00:00:00","commodity":"Apricots","loss_percentage":9,"crop_yield":106579,"lost_crop_yield":9592.109999999999,"_deepnote_index_column":2},{"year":"2000-01-01 00:00:00","commodity":"Blueberries","loss_percentage":8,"crop_yield":51253,"lost_crop_yield":4100.24,"_deepnote_index_column":3},{"year":"2000-01-01 00:00:00","commodity":"Cantaloupes and other melons","loss_percentage":8,"crop_yield":230769,"lost_crop_yield":18461.52,"_deepnote_index_column":4},{"year":"2000-01-01 00:00:00","commodity":"Cranberries","loss_percentage":4,"crop_yield":172109,"lost_crop_yield":6884.360000000001,"_deepnote_index_column":5}]},"text/plain":"        year                     commodity  loss_percentage  crop_yield  \\\n1 2000-01-01                        Apples              4.0    273483.0   \n2 2000-01-01                      Apricots              9.0    106579.0   \n3 2000-01-01                   Blueberries              8.0     51253.0   \n4 2000-01-01  Cantaloupes and other melons              8.0    230769.0   \n5 2000-01-01                   Cranberries              4.0    172109.0   \n\n   lost_crop_yield  \n1         10939.32  \n2          9592.11  \n3          4100.24  \n4         18461.52  \n5          6884.36  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>commodity</th>\n      <th>loss_percentage</th>\n      <th>crop_yield</th>\n      <th>lost_crop_yield</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>2000-01-01</td>\n      <td>Apples</td>\n      <td>4.0</td>\n      <td>273483.0</td>\n      <td>10939.32</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2000-01-01</td>\n      <td>Apricots</td>\n      <td>9.0</td>\n      <td>106579.0</td>\n      <td>9592.11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2000-01-01</td>\n      <td>Blueberries</td>\n      <td>8.0</td>\n      <td>51253.0</td>\n      <td>4100.24</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2000-01-01</td>\n      <td>Cantaloupes and other melons</td>\n      <td>8.0</td>\n      <td>230769.0</td>\n      <td>18461.52</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2000-01-01</td>\n      <td>Cranberries</td>\n      <td>4.0</td>\n      <td>172109.0</td>\n      <td>6884.36</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737341078,"execution_millis":208,"deepnote_to_be_reexecuted":false,"cell_id":"c5f52df52c2145d08240e70ee282c192","deepnote_cell_type":"code"},"source":"f_group_am = fruits_am.groupby(\"year\").median()[[\"lost_crop_yield\"]] #note choice of median crop yield here\nplt.plot(f_group_am)","block_group":"1f0aaba93f814b0b982281947312a8ca","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7f9515044b80>]"},"metadata":{}},{"data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{"image/png":{"width":569,"height":413}},"output_type":"display_data"}]},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"2dfeb6a4fa1644aaa4d448d1c40fe549","deepnote_cell_type":"text-cell-h2"},"source":"## Stationarity","block_group":"e452f8ec91bf465ba1a1faeee8be88b1"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737341587,"execution_millis":98,"deepnote_to_be_reexecuted":false,"cell_id":"1c95077e899947a980f2b60c66c6cb03","deepnote_cell_type":"code"},"source":"def get_stationarity(time_series):\n    rolling_mean = time_series.rolling(window=4).mean()\n    rolling_std = time_series.rolling(window=4).std()\n\n    # rolling statistics plot\n    original = plt.plot(time_series, color='blue', label='Original')\n    mean = plt.plot(rolling_mean, color='red', label='Rolling Mean')\n    std = plt.plot(rolling_std, color='black', label='Rolling Std')\n    plt.legend(loc='best')\n    plt.title('Rolling Mean & Standard Deviation')\n    plt.show(block=False)\n\n    # Dickey–Fuller test:\n    result = adfuller(time_series['lost_crop_yield'])\n    print('ADF Statistic: {}'.format(result[0]))\n    print('p-value: {}'.format(result[1]))\n    print('Critical Values:')\n    for key, value in result[4].items():\n        print('\\t{}: {}'.format(key, value))","block_group":"0b8d2336b62d4dbbaeccad9d54a145cd","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737341588,"execution_millis":524,"deepnote_to_be_reexecuted":false,"cell_id":"8036c35b674c4e96a398d5bc75e6c434","deepnote_cell_type":"code"},"source":"get_stationarity(f_group_am)","block_group":"be1b2220b003486ca9ab680e967b29ef","execution_count":null,"outputs":[{"data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{},"output_type":"display_data"},{"name":"stdout","text":"ADF Statistic: -0.4524523450229775\np-value: 0.9009659245433874\nCritical Values:\n\t1%: -4.223238279489106\n\t5%: -3.189368925619835\n\t10%: -2.729839421487603\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"ab071872a21349e18d68bd78ee3d4d0d","deepnote_cell_type":"text-cell-p"},"source":"Indicators time series is NOT stationary:","block_group":"3d23948f8d5a40cd81474db099b482e8"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"07013636ff8a413cb952ecca34bff072","deepnote_cell_type":"text-cell-bullet"},"source":"- Rolling mean and rolling STD are still increasing over time, not flat","block_group":"9e1732c8424c4a0fbdd43ef146eb74de"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"977042b15c494f3b9243712d8ea40091","deepnote_cell_type":"text-cell-bullet"},"source":"- P-value of ADF is well above cutoff of 0.05","block_group":"963cf8eacb9148b892dfda5866a97f39"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"5fc16a11b2d44f19bf6c89f6792e276d","deepnote_cell_type":"text-cell-p"},"source":"Because it is not stationary, we need to find some sort of way to convert it to a stationary series before performing ARIMAX => this helps us conclude one of the parameters of the ARIMAX model, d (marks # of steps needed for time series to be converted to a stationary series)","block_group":"3c3e5b2afe184682a10b6e02d37f67f6"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737342041,"execution_millis":494,"deepnote_to_be_reexecuted":false,"cell_id":"836cca542c7b4d9eb5ef56e78815add7","deepnote_cell_type":"code"},"source":"# logged, and shifted\nf_am_log = np.log(f_group_am)\nf_am_s = f_am_log - f_am_log.shift()\nf_am_s.dropna(inplace=True)\n\nget_stationarity(f_am_s)","block_group":"2bb82c6b8cf64e2fb564fec1c7cfce7f","execution_count":null,"outputs":[{"data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{},"output_type":"display_data"},{"name":"stdout","text":"ADF Statistic: -2.7471188327065494\np-value: 0.06623348215215262\nCritical Values:\n\t1%: -4.223238279489106\n\t5%: -3.189368925619835\n\t10%: -2.729839421487603\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"d9b212308ae341b7a2104b2762ac03ea","deepnote_cell_type":"text-cell-p"},"source":"Now:","block_group":"8265d7b287844b1785e74a69afdbc5b1"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"fe69dfc5af644b74a7f8feca10f8ffe1","deepnote_cell_type":"text-cell-bullet"},"source":"- Rolling mean & rolling std much smoother","block_group":"29dc958f5a4e4fb6a773c8ec67151c92"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"e726a9f88d514e56b223fc4a411ec4d2","deepnote_cell_type":"text-cell-bullet"},"source":"- P-value is WELL below critical values","block_group":"ed9f76518150406089fbd724ab9d52b4"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"1f734e4a5b824e888016c99d85f7f564","deepnote_cell_type":"text-cell-p"},"source":"In other words: much more stationary! Another way we can check this is through correlograms (ACF & PACF test).","block_group":"2ad4b2120883463fa45b77ecb0d312bc"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"bdfd065fc827431394073a37472621a4","deepnote_cell_type":"text-cell-h2"},"source":"## ARIMA (no exogenous)","block_group":"42a29a9d4edf40f197f78a2c14f6c2df"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737344786,"execution_millis":133,"deepnote_to_be_reexecuted":false,"cell_id":"05d778dfe643497a8d2f03ae70d7f4a5","deepnote_cell_type":"code"},"source":"f_group_am.index","block_group":"a242d31d002c4c76ae56c67ec739cd4b","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"DatetimeIndex(['2000-01-01', '2001-01-01', '2002-01-01', '2003-01-01',\n               '2004-01-01', '2005-01-01', '2006-01-01', '2007-01-01',\n               '2008-01-01', '2009-01-01', '2010-01-01', '2011-01-01',\n               '2012-01-01', '2013-01-01', '2014-01-01', '2015-01-01',\n               '2016-01-01', '2017-01-01', '2018-01-01', '2019-01-01'],\n              dtype='datetime64[ns]', name='year', freq=None)"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737344787,"execution_millis":129,"deepnote_to_be_reexecuted":false,"cell_id":"b6a867635050471cbed16d7fa3b4c46d","deepnote_cell_type":"code"},"source":"# create train/test split so we can see how accurate our model is\ntr_start,tr_end = '1985-01-01','2014-01-01'\nte_start,te_end = '2015-01-01','2020-01-01'\ntra = f_group_am[\"lost_crop_yield\"][tr_start:tr_end].dropna()\ntes = f_group_am[\"lost_crop_yield\"][te_start:te_end].dropna()","block_group":"db1197bf49ad44ad8b711acd8001b32f","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737344787,"execution_millis":15252,"deepnote_to_be_reexecuted":false,"cell_id":"710096dc29c54c12afda9a8ac7289e17","deepnote_cell_type":"code"},"source":"!pip install pmdarima\n#This is to determine the order of the ARIMA model\nfrom pmdarima import auto_arima\n\n# Fit an auto ARIMA model to determine the best parameters (p, d, q)\nauto_arima_model = auto_arima(f_group_am)\n\n# Get the summary of the auto ARIMA model\nprint(auto_arima_model.summary())","block_group":"84d0f914e10a42f9b6e1a5ce573537f5","execution_count":null,"outputs":[{"name":"stdout","text":"Requirement already satisfied: pmdarima in /root/venv/lib/python3.9/site-packages (2.0.4)\nRequirement already satisfied: urllib3 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pmdarima) (1.26.12)\nRequirement already satisfied: numpy>=1.21.2 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pmdarima) (1.23.4)\nRequirement already satisfied: scikit-learn>=0.22 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pmdarima) (1.1.2)\nRequirement already satisfied: pandas>=0.19 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pmdarima) (1.2.5)\nRequirement already satisfied: joblib>=0.11 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pmdarima) (1.2.0)\nRequirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /root/venv/lib/python3.9/site-packages (from pmdarima) (58.1.0)\nRequirement already satisfied: statsmodels>=0.13.2 in /root/venv/lib/python3.9/site-packages (from pmdarima) (0.14.0)\nRequirement already satisfied: packaging>=17.1 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from pmdarima) (21.3)\nRequirement already satisfied: scipy>=1.3.2 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pmdarima) (1.9.3)\nRequirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /root/venv/lib/python3.9/site-packages (from pmdarima) (3.0.6)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from packaging>=17.1->pmdarima) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7.3 in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from pandas>=0.19->pmdarima) (2.8.2)\nRequirement already satisfied: pytz>=2017.3 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from pandas>=0.19->pmdarima) (2022.5)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /shared-libs/python3.9/py/lib/python3.9/site-packages (from scikit-learn>=0.22->pmdarima) (3.1.0)\nRequirement already satisfied: patsy>=0.5.2 in /root/venv/lib/python3.9/site-packages (from statsmodels>=0.13.2->pmdarima) (0.5.4)\nRequirement already satisfied: six in /shared-libs/python3.9/py-core/lib/python3.9/site-packages (from patsy>=0.5.2->statsmodels>=0.13.2->pmdarima) (1.16.0)\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n                               SARIMAX Results                                \n==============================================================================\nDep. Variable:                      y   No. Observations:                   20\nModel:               SARIMAX(2, 1, 2)   Log Likelihood                -153.856\nDate:                Tue, 05 Dec 2023   AIC                            319.713\nTime:                        00:49:19   BIC                            325.379\nSample:                    01-01-2000   HQIC                           320.672\n                         - 01-01-2019                                         \nCovariance Type:                  opg                                         \n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nintercept    434.6916    208.700      2.083      0.037      25.647     843.736\nar.L1         -0.1787      0.162     -1.100      0.271      -0.497       0.140\nar.L2         -0.5831      0.156     -3.748      0.000      -0.888      -0.278\nma.L1         -0.5334      0.191     -2.787      0.005      -0.909      -0.158\nma.L2          0.9137      0.425      2.152      0.031       0.081       1.746\nsigma2      3.547e+05      2e+05      1.777      0.076   -3.64e+04    7.46e+05\n===================================================================================\nLjung-Box (L1) (Q):                   1.11   Jarque-Bera (JB):                 0.44\nProb(Q):                              0.29   Prob(JB):                         0.80\nHeteroskedasticity (H):               2.81   Skew:                            -0.14\nProb(H) (two-sided):                  0.23   Kurtosis:                         2.31\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n","output_type":"stream"}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737360035,"execution_millis":955,"deepnote_to_be_reexecuted":false,"cell_id":"3af526e04ea74dc8a1252815bf6c3842","deepnote_cell_type":"code"},"source":"model = SARIMAX(tra, order=(2, 1, 2),enforce_invertibility=False, enforce_stationarity=False)\nresults = model.fit()\n\n# Forecast future periods (change 'steps' to the number of periods you want to forecast)\nforecast = results.get_forecast(steps=5)  # For example, forecasting 10 steps ahead\n\n# Extract confidence intervals for the forecast\nconfidence_intervals = forecast.conf_int()\n\n# Plot the forecasted values\nplt.figure(figsize=(10, 6))\nplt.plot(tra.index, tra.values, label='Observed')  # Assuming 'data' is a pandas Series or DataFrame\n\n# Plot the forecasted values\nplt.plot(forecast.predicted_mean.index, forecast.predicted_mean.values, color='red', label='Forecast')\n\n# Shade the confidence interval\nplt.fill_between(\n    confidence_intervals.index,\n    confidence_intervals.iloc[:, 0],\n    confidence_intervals.iloc[:, 1],\n    color='pink',  # Adjust the color as needed\n    alpha=0.3  # Adjust transparency if desired\n)\n\nplt.plot(tes.index, tes.values, label='actual')\nplt.title('ARIMA Forecast with Confidence Intervals')\nplt.xlabel('Time')\nplt.ylabel('Values')\nplt.legend()\nplt.show()\n\nprint(\"RMSE: \", np.mean((forecast.predicted_mean.values - tes.values)**2)**.5 )","block_group":"cbf5bb536cdb458ca56ee0bdb6b002f0","execution_count":null,"outputs":[{"name":"stdout","text":"RUNNING THE L-BFGS-B CODE\n\n           * * *\n\nMachine precision = 2.220D-16\n N =            5     M =           10\n\nAt X0         0 variables are exactly at the bounds\n\nAt iterate    0    f=  6.31480D+00    |proj g|=  8.58037D-01\n\nAt iterate    5    f=  5.93777D+00    |proj g|=  2.19071D-01\n\nAt iterate   10    f=  5.78125D+00    |proj g|=  1.52168D-01\n\nAt iterate   15    f=  5.75715D+00    |proj g|=  5.34636D-04\n\nAt iterate   20    f=  5.75714D+00    |proj g|=  5.12245D-04\n\nAt iterate   25    f=  5.75688D+00    |proj g|=  8.55592D-03\n\nAt iterate   30    f=  5.73711D+00    |proj g|=  5.27358D-02\n\nAt iterate   35    f=  5.73283D+00    |proj g|=  2.74915D-03\n\n           * * *\n\nTit   = total number of iterations\nTnf   = total number of function evaluations\nTnint = total number of segments explored during Cauchy searches\nSkip  = number of BFGS updates skipped\nNact  = number of active bounds at final generalized Cauchy point\nProjg = norm of the final projected gradient\nF     = final function value\n\n           * * *\n\n   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n    5     39     46      1     0     0   6.363D-07   5.733D+00\n  F =   5.7328073968413671     \n\nCONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL            \n/root/venv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n  self._init_dates(dates, freq)\n/root/venv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n  self._init_dates(dates, freq)\n This problem is unconstrained.\n","output_type":"stream"},{"data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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\n"},"metadata":{},"output_type":"display_data"},{"name":"stdout","text":"RMSE:  1449.9662353152148\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"bf067a2f32974ede969fb0291a66bdf3","deepnote_cell_type":"text-cell-h2"},"source":"## ARIMAX (exogenous)","block_group":"7593f876841d4e21a4eeeb49e218c818"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"5acbf9addfc34614b13ab52c29e62cd0","deepnote_cell_type":"text-cell-p"},"source":"Next steps:","block_group":"2aeaf918ae1a4c519816c11241c90597"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"b320466a3c9342939d6a06d9a450aeaa","deepnote_cell_type":"text-cell-bullet"},"source":"- Find correlation with other variables from other df's, use the top few to train a new model and see if confidence interval reduces","block_group":"95b226bf545b40c7bcb90459cf2fefa9"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"24d88c8e760b4eaea80b5556e4886892","deepnote_cell_type":"text-cell-bullet"},"source":"- Also maybe try other orders of ARIMA?","block_group":"25bb6490e139418aac9156f08d06fa4f"},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"db38a109950e451fa77a85873bc525f0","deepnote_cell_type":"text-cell-bullet"},"source":"- Apply ARIMAX to other products (veg, roots, animal prods)","block_group":"643a884e5f8d4c92825fb4a35f1c210c"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737360956,"execution_millis":544,"deepnote_to_be_reexecuted":false,"cell_id":"b83d310e350149d58dfc9487696bcce7","deepnote_cell_type":"code"},"source":"CO2 = pd.read_csv(\"Raw Data/CO2 Emissions.csv\")\nCO2['Year'] = pd.to_datetime(CO2['Year'].astype(str), format='%Y')\nCO2.head()","block_group":"c42eb14cb3a2423d93e9881eb7c1f1ec","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":2,"row_count":5,"columns":[{"name":"Year","dtype":"datetime64[ns]","stats":{"unique_count":5,"nan_count":0,"min":"1990-01-01 00:00:00","max":"1994-01-01 00:00:00","histogram":[{"bin_start":631152000000000000,"bin_end":643775040000000000,"count":1},{"bin_start":643775040000000000,"bin_end":656398080000000000,"count":0},{"bin_start":656398080000000000,"bin_end":669021120000000000,"count":1},{"bin_start":669021120000000000,"bin_end":681644160000000000,"count":0},{"bin_start":681644160000000000,"bin_end":694267200000000000,"count":1},{"bin_start":694267200000000000,"bin_end":706890240000000000,"count":0},{"bin_start":706890240000000000,"bin_end":719513280000000000,"count":0},{"bin_start":719513280000000000,"bin_end":732136320000000000,"count":1},{"bin_start":732136320000000000,"bin_end":744759360000000000,"count":0},{"bin_start":744759360000000000,"bin_end":757382400000000000,"count":1}]}},{"name":"CO2  Emissions","dtype":"float64","stats":{"unique_count":5,"nan_count":0,"min":"19.00338958","max":"19.40733586","histogram":[{"bin_start":19.00338958,"bin_end":19.043784208,"count":2},{"bin_start":19.043784208,"bin_end":19.084178836,"count":0},{"bin_start":19.084178836,"bin_end":19.124573464,"count":0},{"bin_start":19.124573464,"bin_end":19.164968092,"count":0},{"bin_start":19.164968092,"bin_end":19.20536272,"count":0},{"bin_start":19.20536272,"bin_end":19.245757348,"count":1},{"bin_start":19.245757348,"bin_end":19.286151976,"count":1},{"bin_start":19.286151976,"bin_end":19.326546604,"count":0},{"bin_start":19.326546604,"bin_end":19.366941232,"count":0},{"bin_start":19.366941232,"bin_end":19.40733586,"count":1}]}},{"name":"_deepnote_index_column","dtype":"int64"}],"rows":[{"Year":"1990-01-01 00:00:00","CO2  Emissions":19.40733586,"_deepnote_index_column":0},{"Year":"1991-01-01 00:00:00","CO2  Emissions":19.00338958,"_deepnote_index_column":1},{"Year":"1992-01-01 00:00:00","CO2  Emissions":19.02284515,"_deepnote_index_column":2},{"Year":"1993-01-01 00:00:00","CO2  Emissions":19.21833148,"_deepnote_index_column":3},{"Year":"1994-01-01 00:00:00","CO2  Emissions":19.25618525,"_deepnote_index_column":4}]},"text/plain":"        Year  CO2  Emissions\n0 1990-01-01       19.407336\n1 1991-01-01       19.003390\n2 1992-01-01       19.022845\n3 1993-01-01       19.218331\n4 1994-01-01       19.256185","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Year</th>\n      <th>CO2  Emissions</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1990-01-01</td>\n      <td>19.407336</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1991-01-01</td>\n      <td>19.003390</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1992-01-01</td>\n      <td>19.022845</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1993-01-01</td>\n      <td>19.218331</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1994-01-01</td>\n      <td>19.256185</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737361499,"execution_millis":336,"deepnote_to_be_reexecuted":false,"cell_id":"93ce24874f6d4de9856d07b5b3da0a8f","deepnote_cell_type":"code"},"source":"fertilizers = pd.read_csv(\"Raw Data/Fertilizers.csv\")\nfertilizers['Year'] = pd.to_datetime(fertilizers['Year'].astype(str), format='%Y')\nfertilizers = fertilizers.groupby(\"Year\").mean()[[\"Value\"]].reset_index()\nfertilizers = fertilizers.rename(columns={\"Value\":\"Fertilizer\"})\nfertilizers.head()","block_group":"10f18cd8153040f88d25fc70a25b0ac6","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":18,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":2,"row_count":5,"columns":[{"name":"Year","dtype":"datetime64[ns]","stats":{"unique_count":5,"nan_count":0,"min":"1990-01-01 00:00:00","max":"1994-01-01 00:00:00","histogram":[{"bin_start":631152000000000000,"bin_end":643775040000000000,"count":1},{"bin_start":643775040000000000,"bin_end":656398080000000000,"count":0},{"bin_start":656398080000000000,"bin_end":669021120000000000,"count":1},{"bin_start":669021120000000000,"bin_end":681644160000000000,"count":0},{"bin_start":681644160000000000,"bin_end":694267200000000000,"count":1},{"bin_start":694267200000000000,"bin_end":706890240000000000,"count":0},{"bin_start":706890240000000000,"bin_end":719513280000000000,"count":0},{"bin_start":719513280000000000,"bin_end":732136320000000000,"count":1},{"bin_start":732136320000000000,"bin_end":744759360000000000,"count":0},{"bin_start":744759360000000000,"bin_end":757382400000000000,"count":1}]}},{"name":"Fertilizer","dtype":"float64","stats":{"unique_count":5,"nan_count":0,"min":"6216511.843333334","max":"6632870.966666668","histogram":[{"bin_start":6216511.843333334,"bin_end":6258147.755666668,"count":2},{"bin_start":6258147.755666668,"bin_end":6299783.6680000005,"count":0},{"bin_start":6299783.6680000005,"bin_end":6341419.580333333,"count":1},{"bin_start":6341419.580333333,"bin_end":6383055.492666667,"count":0},{"bin_start":6383055.492666667,"bin_end":6424691.405000001,"count":0},{"bin_start":6424691.405000001,"bin_end":6466327.317333334,"count":0},{"bin_start":6466327.317333334,"bin_end":6507963.229666667,"count":0},{"bin_start":6507963.229666667,"bin_end":6549599.142000001,"count":0},{"bin_start":6549599.142000001,"bin_end":6591235.054333335,"count":1},{"bin_start":6591235.054333335,"bin_end":6632870.966666668,"count":1}]}},{"name":"_deepnote_index_column","dtype":"int64"}],"rows":[{"Year":"1990-01-01 00:00:00","Fertilizer":6216511.843333334,"_deepnote_index_column":0},{"Year":"1991-01-01 00:00:00","Fertilizer":6238545.163333334,"_deepnote_index_column":1},{"Year":"1992-01-01 00:00:00","Fertilizer":6307994.496666667,"_deepnote_index_column":2},{"Year":"1993-01-01 00:00:00","Fertilizer":6632870.966666668,"_deepnote_index_column":3},{"Year":"1994-01-01 00:00:00","Fertilizer":6553303.393333334,"_deepnote_index_column":4}]},"text/plain":"        Year    Fertilizer\n0 1990-01-01  6.216512e+06\n1 1991-01-01  6.238545e+06\n2 1992-01-01  6.307994e+06\n3 1993-01-01  6.632871e+06\n4 1994-01-01  6.553303e+06","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      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class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>GNI Per Capita (US $)</th>\n      <th>Annual % Growth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1991-01-01</td>\n      <td>24060</td>\n      <td>1.22</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1992-01-01</td>\n      <td>24270</td>\n      <td>0.87</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1993-01-01</td>\n      <td>25680</td>\n      <td>5.81</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1994-01-01</td>\n      <td>26390</td>\n      <td>2.76</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1995-01-01</td>\n      <td>27650</td>\n      <td>4.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737362567,"execution_millis":320,"deepnote_to_be_reexecuted":false,"cell_id":"fb943e2616f345838a6ac9daa5bd5aac","deepnote_cell_type":"code"},"source":"precipitation = pd.read_csv(\"Raw Data/Precipitation.csv\")\nprecipitation['Year'] = pd.to_datetime(precipitation['Year'].astype(str), format='%Y')\nprecipitation.head()","block_group":"70fc1bccf6c94d92aa6cd10624a8aeb1","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":21,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":2,"row_count":5,"columns":[{"name":"Year","dtype":"datetime64[ns]","stats":{"unique_count":5,"nan_count":0,"min":"1990-01-01 00:00:00","max":"1994-01-01 00:00:00","histogram":[{"bin_start":631152000000000000,"bin_end":643775040000000000,"count":1},{"bin_start":643775040000000000,"bin_end":656398080000000000,"count":0},{"bin_start":656398080000000000,"bin_end":669021120000000000,"count":1},{"bin_start":669021120000000000,"bin_end":681644160000000000,"count":0},{"bin_start":681644160000000000,"bin_end":694267200000000000,"count":1},{"bin_start":694267200000000000,"bin_end":706890240000000000,"count":0},{"bin_start":706890240000000000,"bin_end":719513280000000000,"count":0},{"bin_start":719513280000000000,"bin_end":732136320000000000,"count":1},{"bin_start":732136320000000000,"bin_end":744759360000000000,"count":0},{"bin_start":744759360000000000,"bin_end":757382400000000000,"count":1}]}},{"name":"Avg. Precipitation (inches)","dtype":"float64","stats":{"unique_count":5,"nan_count":0,"min":"30.6","max":"32.6","histogram":[{"bin_start":30.6,"bin_end":30.8,"count":1},{"bin_start":30.8,"bin_end":31,"count":0},{"bin_start":31,"bin_end":31.200000000000003,"count":0},{"bin_start":31.200000000000003,"bin_end":31.400000000000002,"count":1},{"bin_start":31.400000000000002,"bin_end":31.6,"count":0},{"bin_start":31.6,"bin_end":31.8,"count":0},{"bin_start":31.8,"bin_end":32,"count":0},{"bin_start":32,"bin_end":32.2,"count":0},{"bin_start":32.2,"bin_end":32.4,"count":1},{"bin_start":32.4,"bin_end":32.6,"count":2}]}},{"name":"_deepnote_index_column","dtype":"int64"}],"rows":[{"Year":"1990-01-01 00:00:00","Avg. Precipitation (inches)":32.2,"_deepnote_index_column":0},{"Year":"1991-01-01 00:00:00","Avg. Precipitation (inches)":32.4,"_deepnote_index_column":1},{"Year":"1992-01-01 00:00:00","Avg. Precipitation (inches)":31.3,"_deepnote_index_column":2},{"Year":"1993-01-01 00:00:00","Avg. Precipitation (inches)":32.6,"_deepnote_index_column":3},{"Year":"1994-01-01 00:00:00","Avg. Precipitation (inches)":30.6,"_deepnote_index_column":4}]},"text/plain":"        Year  Avg. Precipitation (inches)\n0 1990-01-01                         32.2\n1 1991-01-01                         32.4\n2 1992-01-01                         31.3\n3 1993-01-01                         32.6\n4 1994-01-01                         30.6","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Year</th>\n      <th>Avg. Precipitation (inches)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1990-01-01</td>\n      <td>32.2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1991-01-01</td>\n      <td>32.4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1992-01-01</td>\n      <td>31.3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1993-01-01</td>\n      <td>32.6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1994-01-01</td>\n      <td>30.6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737362889,"execution_millis":149,"deepnote_table_state":{"sortBy":[],"filters":[],"pageSize":10,"pageIndex":1},"deepnote_table_loading":false,"deepnote_to_be_reexecuted":false,"cell_id":"c2a7fb49a2334add8434e1fd31a6589b","deepnote_cell_type":"code"},"source":"a = pd.merge(f_group_am.reset_index(), CO2, how='left', left_on='year', right_on='Year')\nb = pd.merge(a, fertilizers, how='left', left_on='year', right_on='Year')\nc = pd.merge(b, gdp, how='left', left_on='year', right_on='Year')\nd = pd.merge(c, precipitation, how='left', left_on='year', right_on='Year')\ne = pd.merge(d, gni, how='left', left_on='year', right_on='date')\ncorr_tbl = e[[\"year\", \"lost_crop_yield\", \"CO2  Emissions\", \"Fertilizer\", \"GDP\", \"GNI Per Capita (US $)\", \"Annual % Growth\", \"Avg. Precipitation (inches)\"]]\ncorr_tbl.corr()","block_group":"44c0303ef09a420aa133dae0b5fb4459","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":22,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":7,"row_count":7,"columns":[{"name":"lost_crop_yield","dtype":"float64","stats":{"unique_count":7,"nan_count":0,"min":"-0.7842748711754718","max":"1.0","histogram":[{"bin_start":-0.7842748711754718,"bin_end":-0.6058473840579246,"count":1},{"bin_start":-0.6058473840579246,"bin_end":-0.42741989694037746,"count":0},{"bin_start":-0.42741989694037746,"bin_end":-0.24899240982283033,"count":0},{"bin_start":-0.24899240982283033,"bin_end":-0.07056492270528314,"count":0},{"bin_start":-0.07056492270528314,"bin_end":0.10786256441226405,"count":1},{"bin_start":0.10786256441226405,"bin_end":0.28629005152981113,"count":1},{"bin_start":0.28629005152981113,"bin_end":0.46471753864735843,"count":1},{"bin_start":0.46471753864735843,"bin_end":0.6431450257649055,"count":0},{"bin_start":0.6431450257649055,"bin_end":0.8215725128824526,"count":2},{"bin_start":0.8215725128824526,"bin_end":1,"count":1}]}},{"name":"CO2  Emissions","dtype":"float64","stats":{"unique_count":7,"nan_count":0,"min":"-0.9399826984633074","max":"1.0","histogram":[{"bin_start":-0.9399826984633074,"bin_end":-0.7459844286169767,"count":3},{"bin_start":-0.7459844286169767,"bin_end":-0.5519861587706459,"count":1},{"bin_start":-0.5519861587706459,"bin_end":-0.3579878889243152,"count":0},{"bin_start":-0.3579878889243152,"bin_end":-0.16398961907798448,"count":1},{"bin_start":-0.16398961907798448,"bin_end":0.030008650768346246,"count":0},{"bin_start":0.030008650768346246,"bin_end":0.22400692061467697,"count":0},{"bin_start":0.22400692061467697,"bin_end":0.4180051904610077,"count":1},{"bin_start":0.4180051904610077,"bin_end":0.6120034603073384,"count":0},{"bin_start":0.6120034603073384,"bin_end":0.8060017301536692,"count":0},{"bin_start":0.8060017301536692,"bin_end":1,"count":1}]}},{"name":"Fertilizer","dtype":"float64","stats":{"unique_count":7,"nan_count":0,"min":"-0.30847158893072946","max":"1.0","histogram":[{"bin_start":-0.30847158893072946,"bin_end":-0.17762443003765652,"count":1},{"bin_start":-0.17762443003765652,"bin_end":-0.046777271144583576,"count":0},{"bin_start":-0.046777271144583576,"bin_end":0.08406988774848934,"count":1},{"bin_start":0.08406988774848934,"bin_end":0.2149170466415623,"count":3},{"bin_start":0.2149170466415623,"bin_end":0.34576420553463527,"count":1},{"bin_start":0.34576420553463527,"bin_end":0.47661136442770813,"count":0},{"bin_start":0.47661136442770813,"bin_end":0.6074585233207811,"count":0},{"bin_start":0.6074585233207811,"bin_end":0.7383056822138541,"count":0},{"bin_start":0.7383056822138541,"bin_end":0.8691528411069269,"count":0},{"bin_start":0.8691528411069269,"bin_end":1,"count":1}]}},{"name":"GDP","dtype":"float64","stats":{"unique_count":7,"nan_count":0,"min":"-0.8707192199656145","max":"1.0","histogram":[{"bin_start":-0.8707192199656145,"bin_end":-0.6836472979690531,"count":1},{"bin_start":-0.6836472979690531,"bin_end":-0.49657537597249163,"count":0},{"bin_start":-0.49657537597249163,"bin_end":-0.30950345397593015,"count":0},{"bin_start":-0.30950345397593015,"bin_end":-0.12243153197936874,"count":0},{"bin_start":-0.12243153197936874,"bin_end":0.06464039001719268,"count":1},{"bin_start":0.06464039001719268,"bin_end":0.2517123120137542,"count":1},{"bin_start":0.2517123120137542,"bin_end":0.4387842340103155,"count":0},{"bin_start":0.4387842340103155,"bin_end":0.625856156006877,"count":0},{"bin_start":0.625856156006877,"bin_end":0.8129280780034386,"count":2},{"bin_start":0.8129280780034386,"bin_end":1,"count":2}]}},{"name":"GNI Per Capita (US $)","dtype":"float64","stats":{"unique_count":7,"nan_count":0,"min":"-0.9399826984633074","max":"1.0","histogram":[{"bin_start":-0.9399826984633074,"bin_end":-0.7459844286169767,"count":1},{"bin_start":-0.7459844286169767,"bin_end":-0.5519861587706459,"count":0},{"bin_start":-0.5519861587706459,"bin_end":-0.3579878889243152,"count":0},{"bin_start":-0.3579878889243152,"bin_end":-0.16398961907798448,"count":1},{"bin_start":-0.16398961907798448,"bin_end":0.030008650768346246,"count":0},{"bin_start":0.030008650768346246,"bin_end":0.22400692061467697,"count":1},{"bin_start":0.22400692061467697,"bin_end":0.4180051904610077,"count":0},{"bin_start":0.4180051904610077,"bin_end":0.6120034603073384,"count":0},{"bin_start":0.6120034603073384,"bin_end":0.8060017301536692,"count":2},{"bin_start":0.8060017301536692,"bin_end":1,"count":2}]}},{"name":"Annual % Growth","dtype":"float64","stats":{"unique_count":7,"nan_count":0,"min":"-0.17480077825973553","max":"1.0","histogram":[{"bin_start":-0.17480077825973553,"bin_end":-0.05732070043376197,"count":2},{"bin_start":-0.05732070043376197,"bin_end":0.06015937739221158,"count":2},{"bin_start":0.06015937739221158,"bin_end":0.1776394552181851,"count":1},{"bin_start":0.1776394552181851,"bin_end":0.2951195330441587,"count":0},{"bin_start":0.2951195330441587,"bin_end":0.4125996108701323,"count":1},{"bin_start":0.4125996108701323,"bin_end":0.5300796886961058,"count":0},{"bin_start":0.5300796886961058,"bin_end":0.6475597665220794,"count":0},{"bin_start":0.6475597665220794,"bin_end":0.7650398443480529,"count":0},{"bin_start":0.7650398443480529,"bin_end":0.8825199221740264,"count":0},{"bin_start":0.8825199221740264,"bin_end":1,"count":1}]}},{"name":"Avg. Precipitation (inches)","dtype":"float64","stats":{"unique_count":7,"nan_count":0,"min":"-0.5572347626676162","max":"1.0","histogram":[{"bin_start":-0.5572347626676162,"bin_end":-0.40151128640085454,"count":1},{"bin_start":-0.40151128640085454,"bin_end":-0.24578781013409295,"count":0},{"bin_start":-0.24578781013409295,"bin_end":-0.09006433386733137,"count":0},{"bin_start":-0.09006433386733137,"bin_end":0.06565914239943027,"count":1},{"bin_start":0.06565914239943027,"bin_end":0.2213826186661919,"count":1},{"bin_start":0.2213826186661919,"bin_end":0.37710609493295344,"count":0},{"bin_start":0.37710609493295344,"bin_end":0.5328295711997151,"count":1},{"bin_start":0.5328295711997151,"bin_end":0.6885530474664767,"count":2},{"bin_start":0.6885530474664767,"bin_end":0.8442765237332384,"count":0},{"bin_start":0.8442765237332384,"bin_end":1,"count":1}]}},{"name":"_deepnote_index_column","dtype":"object"}],"rows":[{"lost_crop_yield":1,"CO2  Emissions":-0.7842748711754718,"Fertilizer":0.15128009134301193,"GDP":0.7896891772603112,"GNI Per Capita (US $)":0.7971038000007156,"Annual % Growth":-0.059646935009807334,"Avg. Precipitation (inches)":0.45319464472953813,"_deepnote_index_column":"lost_crop_yield"},{"lost_crop_yield":-0.7842748711754718,"CO2  Emissions":1,"Fertilizer":-0.30847158893072946,"GDP":-0.8707192199656145,"GNI Per Capita (US $)":-0.9399826984633074,"Annual % Growth":0.3194883675921716,"Avg. Precipitation (inches)":-0.5572347626676162,"_deepnote_index_column":"CO2  Emissions"},{"lost_crop_yield":0.15128009134301193,"CO2  Emissions":-0.30847158893072946,"Fertilizer":1,"GDP":0.2284261898859282,"GNI Per Capita (US $)":0.20166082099116872,"Annual % Growth":0.11150832929251356,"Avg. Precipitation (inches)":0.08059729578289808,"_deepnote_index_column":"Fertilizer"},{"lost_crop_yield":0.7896891772603112,"CO2  Emissions":-0.8707192199656145,"Fertilizer":0.2284261898859282,"GDP":1,"GNI Per Capita (US $)":0.9686063633900955,"Annual % Growth":-0.0538543348270488,"Avg. Precipitation (inches)":0.6641445089608987,"_deepnote_index_column":"GDP"},{"lost_crop_yield":0.7971038000007156,"CO2  Emissions":-0.9399826984633074,"Fertilizer":0.20166082099116872,"GDP":0.9686063633900955,"GNI Per Capita (US $)":1,"Annual % Growth":-0.17480077825973553,"Avg. Precipitation (inches)":0.6351348401763508,"_deepnote_index_column":"GNI Per Capita (US $)"},{"lost_crop_yield":-0.059646935009807334,"CO2  Emissions":0.3194883675921716,"Fertilizer":0.11150832929251356,"GDP":-0.0538543348270488,"GNI Per Capita (US $)":-0.17480077825973553,"Annual % Growth":1,"Avg. Precipitation (inches)":-0.022692900261493093,"_deepnote_index_column":"Annual % Growth"},{"lost_crop_yield":0.45319464472953813,"CO2  Emissions":-0.5572347626676162,"Fertilizer":0.08059729578289808,"GDP":0.6641445089608987,"GNI Per Capita (US $)":0.6351348401763508,"Annual % Growth":-0.022692900261493093,"Avg. Precipitation (inches)":1,"_deepnote_index_column":"Avg. Precipitation (inches)"}]},"text/plain":"                             lost_crop_yield  CO2  Emissions  Fertilizer  \\\nlost_crop_yield                     1.000000       -0.784275    0.151280   \nCO2  Emissions                     -0.784275        1.000000   -0.308472   \nFertilizer                          0.151280       -0.308472    1.000000   \nGDP                                 0.789689       -0.870719    0.228426   \nGNI Per Capita (US $)               0.797104       -0.939983    0.201661   \nAnnual % Growth                    -0.059647        0.319488    0.111508   \nAvg. Precipitation (inches)         0.453195       -0.557235    0.080597   \n\n                                  GDP  GNI Per Capita (US $)  Annual % Growth  \\\nlost_crop_yield              0.789689               0.797104        -0.059647   \nCO2  Emissions              -0.870719              -0.939983         0.319488   \nFertilizer                   0.228426               0.201661         0.111508   \nGDP                          1.000000               0.968606        -0.053854   \nGNI Per Capita (US $)        0.968606               1.000000        -0.174801   \nAnnual % Growth             -0.053854              -0.174801         1.000000   \nAvg. Precipitation (inches)  0.664145               0.635135        -0.022693   \n\n                             Avg. Precipitation (inches)  \nlost_crop_yield                                 0.453195  \nCO2  Emissions                                 -0.557235  \nFertilizer                                      0.080597  \nGDP                                             0.664145  \nGNI Per Capita (US $)                           0.635135  \nAnnual % Growth                                -0.022693  \nAvg. Precipitation (inches)                     1.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>lost_crop_yield</th>\n      <th>CO2  Emissions</th>\n      <th>Fertilizer</th>\n      <th>GDP</th>\n      <th>GNI Per Capita (US $)</th>\n      <th>Annual % Growth</th>\n      <th>Avg. Precipitation (inches)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>lost_crop_yield</th>\n      <td>1.000000</td>\n      <td>-0.784275</td>\n      <td>0.151280</td>\n      <td>0.789689</td>\n      <td>0.797104</td>\n      <td>-0.059647</td>\n      <td>0.453195</td>\n    </tr>\n    <tr>\n      <th>CO2  Emissions</th>\n      <td>-0.784275</td>\n      <td>1.000000</td>\n      <td>-0.308472</td>\n      <td>-0.870719</td>\n      <td>-0.939983</td>\n      <td>0.319488</td>\n      <td>-0.557235</td>\n    </tr>\n    <tr>\n      <th>Fertilizer</th>\n      <td>0.151280</td>\n      <td>-0.308472</td>\n      <td>1.000000</td>\n      <td>0.228426</td>\n      <td>0.201661</td>\n      <td>0.111508</td>\n      <td>0.080597</td>\n    </tr>\n    <tr>\n      <th>GDP</th>\n      <td>0.789689</td>\n      <td>-0.870719</td>\n      <td>0.228426</td>\n      <td>1.000000</td>\n      <td>0.968606</td>\n      <td>-0.053854</td>\n      <td>0.664145</td>\n    </tr>\n    <tr>\n      <th>GNI Per Capita (US $)</th>\n      <td>0.797104</td>\n      <td>-0.939983</td>\n      <td>0.201661</td>\n      <td>0.968606</td>\n      <td>1.000000</td>\n      <td>-0.174801</td>\n      <td>0.635135</td>\n    </tr>\n    <tr>\n      <th>Annual % Growth</th>\n      <td>-0.059647</td>\n      <td>0.319488</td>\n      <td>0.111508</td>\n      <td>-0.053854</td>\n      <td>-0.174801</td>\n      <td>1.000000</td>\n      <td>-0.022693</td>\n    </tr>\n    <tr>\n      <th>Avg. Precipitation (inches)</th>\n      <td>0.453195</td>\n      <td>-0.557235</td>\n      <td>0.080597</td>\n      <td>0.664145</td>\n      <td>0.635135</td>\n      <td>-0.022693</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"formattedRanges":[],"cell_id":"226e5bdd29c44935b74530559509a0fe","deepnote_cell_type":"text-cell-p"},"source":"Looks like CO2, GDP, and GNI are the strongest correlated features => let's insure linear is the best way to depict their relationship","block_group":"7bcbe8d179644b0392e5d689f983a093"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701738014824,"execution_millis":12,"deepnote_to_be_reexecuted":false,"cell_id":"ba1937f6c67f47b1854b5a123de4eade","deepnote_cell_type":"code"},"source":"plt.scatter(corr_tbl[\"lost_crop_yield\"], corr_tbl[\"CO2  Emissions\"])\nplt.ylabel('CO2 Emissions')\nplt.xlabel('Lost Crop')","block_group":"b34ed4eadacd445c8cfdfaa18510c8e9","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":52,"data":{"text/plain":"Text(0.5, 0, 'Lost Crop')"},"metadata":{}},{"data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{"image/png":{"width":563,"height":432}},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701738056755,"execution_millis":380,"deepnote_to_be_reexecuted":false,"cell_id":"093a144db6ab48b79d9442c7125d6399","deepnote_cell_type":"code"},"source":"plt.scatter(corr_tbl[\"lost_crop_yield\"], corr_tbl[\"GDP\"])\nplt.xlabel('Lost Crop')\nplt.ylabel('GDP')","block_group":"704a96b640ad4d6ebc7e5ef73f400314","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":53,"data":{"text/plain":"Text(0, 0.5, 'GDP')"},"metadata":{}},{"data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701738115305,"execution_millis":65,"deepnote_to_be_reexecuted":false,"cell_id":"f0cc6246f0974b2fba1bfcbd6b237049","deepnote_cell_type":"code"},"source":"plt.scatter(corr_tbl[\"lost_crop_yield\"], corr_tbl[\"GNI Per Capita (US $)\"])\nplt.xlabel('Lost Crop')\nplt.ylabel('GNI Per Capita')","block_group":"6912c438dfe34c809cf3509bfb2fc766","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":54,"data":{"text/plain":"Text(0, 0.5, 'GNI Per Capita')"},"metadata":{}},{"data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{"image/png":{"width":589,"height":432}},"output_type":"display_data"}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737364292,"execution_millis":13,"deepnote_table_state":{"sortBy":[],"filters":[],"pageSize":10,"pageIndex":3},"deepnote_table_loading":false,"deepnote_to_be_reexecuted":false,"cell_id":"3724ea2ffadd40b2bc85c7b8adaed3fc","deepnote_cell_type":"code"},"source":"f_group_am","block_group":"931f46615faa4bb6ae1fac3b8a5ae9b8","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":26,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":1,"row_count":20,"columns":[{"name":"lost_crop_yield","dtype":"float64","stats":{"unique_count":20,"nan_count":0,"min":"11013.044999999998","max":"16560.09","histogram":[{"bin_start":11013.044999999998,"bin_end":11567.749499999998,"count":1},{"bin_start":11567.749499999998,"bin_end":12122.453999999998,"count":3},{"bin_start":12122.453999999998,"bin_end":12677.158499999998,"count":3},{"bin_start":12677.158499999998,"bin_end":13231.863,"count":4},{"bin_start":13231.863,"bin_end":13786.5675,"count":3},{"bin_start":13786.5675,"bin_end":14341.271999999999,"count":3},{"bin_start":14341.271999999999,"bin_end":14895.976499999999,"count":1},{"bin_start":14895.976499999999,"bin_end":15450.681,"count":1},{"bin_start":15450.681,"bin_end":16005.3855,"count":0},{"bin_start":16005.3855,"bin_end":16560.09,"count":1}]}},{"name":"_deepnote_index_column","dtype":"datetime64[ns]"}],"rows":[{"lost_crop_yield":13188.875,"_deepnote_index_column":"2010-01-01 00:00:00"},{"lost_crop_yield":13828.82,"_deepnote_index_column":"2011-01-01 00:00:00"},{"lost_crop_yield":13712.61,"_deepnote_index_column":"2012-01-01 00:00:00"},{"lost_crop_yield":14127.58,"_deepnote_index_column":"2013-01-01 00:00:00"},{"lost_crop_yield":13816.205000000002,"_deepnote_index_column":"2014-01-01 00:00:00"},{"lost_crop_yield":12758.34,"_deepnote_index_column":"2015-01-01 00:00:00"},{"lost_crop_yield":15125.86,"_deepnote_index_column":"2016-01-01 00:00:00"},{"lost_crop_yield":14358.765000000001,"_deepnote_index_column":"2017-01-01 00:00:00"},{"lost_crop_yield":13401.725000000002,"_deepnote_index_column":"2018-01-01 00:00:00"},{"lost_crop_yield":16560.09,"_deepnote_index_column":"2019-01-01 00:00:00"}]},"text/plain":"            lost_crop_yield\nyear                       \n2000-01-01        12501.675\n2001-01-01        11013.045\n2002-01-01        11787.780\n2003-01-01        11799.940\n2004-01-01        12796.960\n2005-01-01        12894.710\n2006-01-01        12017.450\n2007-01-01        12262.330\n2008-01-01        13440.835\n2009-01-01        12444.140\n2010-01-01        13188.875\n2011-01-01        13828.820\n2012-01-01        13712.610\n2013-01-01        14127.580\n2014-01-01        13816.205\n2015-01-01        12758.340\n2016-01-01        15125.860\n2017-01-01        14358.765\n2018-01-01        13401.725\n2019-01-01        16560.090","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>lost_crop_yield</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2000-01-01</th>\n      <td>12501.675</td>\n    </tr>\n    <tr>\n      <th>2001-01-01</th>\n      <td>11013.045</td>\n    </tr>\n    <tr>\n      <th>2002-01-01</th>\n      <td>11787.780</td>\n    </tr>\n    <tr>\n      <th>2003-01-01</th>\n      <td>11799.940</td>\n    </tr>\n    <tr>\n      <th>2004-01-01</th>\n      <td>12796.960</td>\n    </tr>\n    <tr>\n      <th>2005-01-01</th>\n      <td>12894.710</td>\n    </tr>\n    <tr>\n      <th>2006-01-01</th>\n      <td>12017.450</td>\n    </tr>\n    <tr>\n      <th>2007-01-01</th>\n      <td>12262.330</td>\n    </tr>\n    <tr>\n      <th>2008-01-01</th>\n      <td>13440.835</td>\n    </tr>\n    <tr>\n      <th>2009-01-01</th>\n      <td>12444.140</td>\n    </tr>\n    <tr>\n      <th>2010-01-01</th>\n      <td>13188.875</td>\n    </tr>\n    <tr>\n      <th>2011-01-01</th>\n      <td>13828.820</td>\n    </tr>\n    <tr>\n      <th>2012-01-01</th>\n      <td>13712.610</td>\n    </tr>\n    <tr>\n      <th>2013-01-01</th>\n      <td>14127.580</td>\n    </tr>\n    <tr>\n      <th>2014-01-01</th>\n      <td>13816.205</td>\n    </tr>\n    <tr>\n      <th>2015-01-01</th>\n      <td>12758.340</td>\n    </tr>\n    <tr>\n      <th>2016-01-01</th>\n      <td>15125.860</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>14358.765</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>13401.725</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>16560.090</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737364293,"execution_millis":46,"deepnote_table_state":{"sortBy":[],"filters":[],"pageSize":10,"pageIndex":0},"deepnote_table_loading":false,"deepnote_to_be_reexecuted":false,"cell_id":"598c7067576243dfbee70989a337ba57","deepnote_cell_type":"code"},"source":"f_amx = f_group_am.loc['2000-01-01':'2015-01-01',:] #2000 to 2015\nf_amx","block_group":"0c786b2b885c4345b09430b406a4e5de","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":27,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":1,"row_count":16,"columns":[{"name":"lost_crop_yield","dtype":"float64","stats":{"unique_count":16,"nan_count":0,"min":"11013.044999999998","max":"14127.58","histogram":[{"bin_start":11013.044999999998,"bin_end":11324.498499999998,"count":1},{"bin_start":11324.498499999998,"bin_end":11635.952,"count":0},{"bin_start":11635.952,"bin_end":11947.405499999999,"count":2},{"bin_start":11947.405499999999,"bin_end":12258.858999999999,"count":1},{"bin_start":12258.858999999999,"bin_end":12570.3125,"count":3},{"bin_start":12570.3125,"bin_end":12881.766,"count":2},{"bin_start":12881.766,"bin_end":13193.2195,"count":2},{"bin_start":13193.2195,"bin_end":13504.672999999999,"count":1},{"bin_start":13504.672999999999,"bin_end":13816.1265,"count":1},{"bin_start":13816.1265,"bin_end":14127.58,"count":3}]}},{"name":"_deepnote_index_column","dtype":"datetime64[ns]"}],"rows":[{"lost_crop_yield":12501.675,"_deepnote_index_column":"2000-01-01 00:00:00"},{"lost_crop_yield":11013.044999999998,"_deepnote_index_column":"2001-01-01 00:00:00"},{"lost_crop_yield":11787.779999999999,"_deepnote_index_column":"2002-01-01 00:00:00"},{"lost_crop_yield":11799.939999999999,"_deepnote_index_column":"2003-01-01 00:00:00"},{"lost_crop_yield":12796.960000000001,"_deepnote_index_column":"2004-01-01 00:00:00"},{"lost_crop_yield":12894.71,"_deepnote_index_column":"2005-01-01 00:00:00"},{"lost_crop_yield":12017.45,"_deepnote_index_column":"2006-01-01 00:00:00"},{"lost_crop_yield":12262.33,"_deepnote_index_column":"2007-01-01 00:00:00"},{"lost_crop_yield":13440.835,"_deepnote_index_column":"2008-01-01 00:00:00"},{"lost_crop_yield":12444.14,"_deepnote_index_column":"2009-01-01 00:00:00"}]},"text/plain":"            lost_crop_yield\nyear                       \n2000-01-01        12501.675\n2001-01-01        11013.045\n2002-01-01        11787.780\n2003-01-01        11799.940\n2004-01-01        12796.960\n2005-01-01        12894.710\n2006-01-01        12017.450\n2007-01-01        12262.330\n2008-01-01        13440.835\n2009-01-01        12444.140\n2010-01-01        13188.875\n2011-01-01        13828.820\n2012-01-01        13712.610\n2013-01-01        14127.580\n2014-01-01        13816.205\n2015-01-01        12758.340","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>lost_crop_yield</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2000-01-01</th>\n      <td>12501.675</td>\n    </tr>\n    <tr>\n      <th>2001-01-01</th>\n      <td>11013.045</td>\n    </tr>\n    <tr>\n      <th>2002-01-01</th>\n      <td>11787.780</td>\n    </tr>\n    <tr>\n      <th>2003-01-01</th>\n      <td>11799.940</td>\n    </tr>\n    <tr>\n      <th>2004-01-01</th>\n      <td>12796.960</td>\n    </tr>\n    <tr>\n      <th>2005-01-01</th>\n      <td>12894.710</td>\n    </tr>\n    <tr>\n      <th>2006-01-01</th>\n      <td>12017.450</td>\n    </tr>\n    <tr>\n      <th>2007-01-01</th>\n      <td>12262.330</td>\n    </tr>\n    <tr>\n      <th>2008-01-01</th>\n      <td>13440.835</td>\n    </tr>\n    <tr>\n      <th>2009-01-01</th>\n      <td>12444.140</td>\n    </tr>\n    <tr>\n      <th>2010-01-01</th>\n      <td>13188.875</td>\n    </tr>\n    <tr>\n      <th>2011-01-01</th>\n      <td>13828.820</td>\n    </tr>\n    <tr>\n      <th>2012-01-01</th>\n      <td>13712.610</td>\n    </tr>\n    <tr>\n      <th>2013-01-01</th>\n      <td>14127.580</td>\n    </tr>\n    <tr>\n      <th>2014-01-01</th>\n      <td>13816.205</td>\n    </tr>\n    <tr>\n      <th>2015-01-01</th>\n      <td>12758.340</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737364341,"execution_millis":11,"deepnote_to_be_reexecuted":false,"cell_id":"3beeca8089bd4ba0ab8b3cfd0ccbc157","deepnote_cell_type":"code"},"source":"f_amx.index","block_group":"f0630b65827c4e9d95330f3a5bf229d1","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":28,"data":{"text/plain":"DatetimeIndex(['2000-01-01', '2001-01-01', '2002-01-01', '2003-01-01',\n               '2004-01-01', '2005-01-01', '2006-01-01', '2007-01-01',\n               '2008-01-01', '2009-01-01', '2010-01-01', '2011-01-01',\n               '2012-01-01', '2013-01-01', '2014-01-01', '2015-01-01'],\n              dtype='datetime64[ns]', name='year', freq=None)"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737412250,"execution_millis":29,"deepnote_table_state":{"sortBy":[],"filters":[],"pageSize":10,"pageIndex":0},"deepnote_table_loading":false,"deepnote_to_be_reexecuted":false,"cell_id":"d7337d8520b347b088dcc5e507d3b4fc","deepnote_cell_type":"code"},"source":"exog_x = corr_tbl[[\"year\", \"CO2  Emissions\", \"GDP\", \"GNI Per Capita (US $)\"]]\nexog_x = exog_x.set_index(\"year\")\nexog_x","block_group":"cd17d220c3fb464fae13a4ad1c9d76ec","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":36,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":3,"row_count":20,"columns":[{"name":"CO2  Emissions","dtype":"float64","stats":{"unique_count":20,"nan_count":0,"min":"14.67338071","max":"20.46979674","histogram":[{"bin_start":14.67338071,"bin_end":15.253022313,"count":4},{"bin_start":15.253022313,"bin_end":15.832663916,"count":2},{"bin_start":15.832663916,"bin_end":16.412305519,"count":2},{"bin_start":16.412305519,"bin_end":16.991947122,"count":2},{"bin_start":16.991947122,"bin_end":17.571588724999998,"count":1},{"bin_start":17.571588724999998,"bin_end":18.151230328,"count":0},{"bin_start":18.151230328,"bin_end":18.730871931,"count":1},{"bin_start":18.730871931,"bin_end":19.310513534000002,"count":2},{"bin_start":19.310513534000002,"bin_end":19.890155137,"count":4},{"bin_start":19.890155137,"bin_end":20.46979674,"count":2}]}},{"name":"GDP","dtype":"float64","stats":{"unique_count":20,"nan_count":0,"min":"50155.7","max":"62478.3","histogram":[{"bin_start":50155.7,"bin_end":51387.96,"count":3},{"bin_start":51387.96,"bin_end":52620.22,"count":1},{"bin_start":52620.22,"bin_end":53852.479999999996,"count":2},{"bin_start":53852.479999999996,"bin_end":55084.74,"count":3},{"bin_start":55084.74,"bin_end":56317,"count":4},{"bin_start":56317,"bin_end":57549.26,"count":2},{"bin_start":57549.26,"bin_end":58781.520000000004,"count":1},{"bin_start":58781.520000000004,"bin_end":60013.78,"count":2},{"bin_start":60013.78,"bin_end":61246.04,"count":0},{"bin_start":61246.04,"bin_end":62478.3,"count":2}]}},{"name":"GNI Per Capita (US $)","dtype":"int64","stats":{"unique_count":20,"nan_count":0,"min":"33680","max":"63460","histogram":[{"bin_start":33680,"bin_end":36658,"count":2},{"bin_start":36658,"bin_end":39636,"count":2},{"bin_start":39636,"bin_end":42614,"count":1},{"bin_start":42614,"bin_end":45592,"count":1},{"bin_start":45592,"bin_end":48570,"count":4},{"bin_start":48570,"bin_end":51548,"count":3},{"bin_start":51548,"bin_end":54526,"count":2},{"bin_start":54526,"bin_end":57504,"count":3},{"bin_start":57504,"bin_end":60482,"count":1},{"bin_start":60482,"bin_end":63460,"count":1}]}},{"name":"_deepnote_index_column","dtype":"datetime64[ns]"}],"rows":[{"CO2  Emissions":20.46979674,"GDP":50175.7,"GNI Per Capita (US $)":33680,"_deepnote_index_column":"2000-01-01 00:00:00"},{"CO2  Emissions":20.17153693,"GDP":50155.7,"GNI Per Capita (US $)":35960,"_deepnote_index_column":"2001-01-01 00:00:00"},{"CO2  Emissions":19.44553028,"GDP":50535.3,"GNI Per Capita (US $)":36700,"_deepnote_index_column":"2002-01-01 00:00:00"},{"CO2  Emissions":19.50650553,"GDP":51503.8,"GNI Per Capita (US $)":37310,"_deepnote_index_column":"2003-01-01 00:00:00"},{"CO2  Emissions":19.59761671,"GDP":52995.2,"GNI Per Capita (US $)":39750,"_deepnote_index_column":"2004-01-01 00:00:00"},{"CO2  Emissions":19.46927252,"GDP":54338,"GNI Per Capita (US $)":43510,"_deepnote_index_column":"2005-01-01 00:00:00"},{"CO2  Emissions":18.94591684,"GDP":55314.2,"GNI Per Capita (US $)":46180,"_deepnote_index_column":"2006-01-01 00:00:00"},{"CO2  Emissions":19.04291178,"GDP":55892.2,"GNI Per Capita (US $)":47830,"_deepnote_index_column":"2007-01-01 00:00:00"},{"CO2  Emissions":18.27849126,"GDP":55433.7,"GNI Per Capita (US $)":48500,"_deepnote_index_column":"2008-01-01 00:00:00"},{"CO2  Emissions":16.80868142,"GDP":53521.2,"GNI Per Capita (US $)":49100,"_deepnote_index_column":"2009-01-01 00:00:00"}]},"text/plain":"            CO2  Emissions      GDP  GNI Per Capita (US $)\nyear                                                      \n2000-01-01       20.469797  50175.7                  33680\n2001-01-01       20.171537  50155.7                  35960\n2002-01-01       19.445530  50535.3                  36700\n2003-01-01       19.506506  51503.8                  37310\n2004-01-01       19.597617  52995.2                  39750\n2005-01-01       19.469273  54338.0                  43510\n2006-01-01       18.945917  55314.2                  46180\n2007-01-01       19.042912  55892.2                  47830\n2008-01-01       18.278491  55433.7                  48500\n2009-01-01       16.808681  53521.2                  49100\n2010-01-01       17.431737  54516.9                  47870\n2011-01-01       16.604190  54960.9                  49150\n2012-01-01       15.789760  55803.5                  50620\n2013-01-01       16.111175  56438.9                  52790\n2014-01-01       16.040917  57308.3                  53990\n2015-01-01       15.560015  58427.6                  55800\n2016-01-01       15.149883  58972.9                  56620\n2017-01-01       14.823245  59914.8                  57140\n2018-01-01       15.222518  61355.6                  59220\n2019-01-01       14.673381  62478.3                  63460","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CO2  Emissions</th>\n      <th>GDP</th>\n      <th>GNI Per Capita (US $)</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2000-01-01</th>\n      <td>20.469797</td>\n      <td>50175.7</td>\n      <td>33680</td>\n    </tr>\n    <tr>\n      <th>2001-01-01</th>\n      <td>20.171537</td>\n      <td>50155.7</td>\n      <td>35960</td>\n    </tr>\n    <tr>\n      <th>2002-01-01</th>\n      <td>19.445530</td>\n      <td>50535.3</td>\n      <td>36700</td>\n    </tr>\n    <tr>\n      <th>2003-01-01</th>\n      <td>19.506506</td>\n      <td>51503.8</td>\n      <td>37310</td>\n    </tr>\n    <tr>\n      <th>2004-01-01</th>\n      <td>19.597617</td>\n      <td>52995.2</td>\n      <td>39750</td>\n    </tr>\n    <tr>\n      <th>2005-01-01</th>\n      <td>19.469273</td>\n      <td>54338.0</td>\n      <td>43510</td>\n    </tr>\n    <tr>\n      <th>2006-01-01</th>\n      <td>18.945917</td>\n      <td>55314.2</td>\n      <td>46180</td>\n    </tr>\n    <tr>\n      <th>2007-01-01</th>\n      <td>19.042912</td>\n      <td>55892.2</td>\n      <td>47830</td>\n    </tr>\n    <tr>\n      <th>2008-01-01</th>\n      <td>18.278491</td>\n      <td>55433.7</td>\n      <td>48500</td>\n    </tr>\n    <tr>\n      <th>2009-01-01</th>\n      <td>16.808681</td>\n      <td>53521.2</td>\n      <td>49100</td>\n    </tr>\n    <tr>\n      <th>2010-01-01</th>\n      <td>17.431737</td>\n      <td>54516.9</td>\n      <td>47870</td>\n    </tr>\n    <tr>\n      <th>2011-01-01</th>\n      <td>16.604190</td>\n      <td>54960.9</td>\n      <td>49150</td>\n    </tr>\n    <tr>\n      <th>2012-01-01</th>\n      <td>15.789760</td>\n      <td>55803.5</td>\n      <td>50620</td>\n    </tr>\n    <tr>\n      <th>2013-01-01</th>\n      <td>16.111175</td>\n      <td>56438.9</td>\n      <td>52790</td>\n    </tr>\n    <tr>\n      <th>2014-01-01</th>\n      <td>16.040917</td>\n      <td>57308.3</td>\n      <td>53990</td>\n    </tr>\n    <tr>\n      <th>2015-01-01</th>\n      <td>15.560015</td>\n      <td>58427.6</td>\n      <td>55800</td>\n    </tr>\n    <tr>\n      <th>2016-01-01</th>\n      <td>15.149883</td>\n      <td>58972.9</td>\n      <td>56620</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>14.823245</td>\n      <td>59914.8</td>\n      <td>57140</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>15.222518</td>\n      <td>61355.6</td>\n      <td>59220</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>14.673381</td>\n      <td>62478.3</td>\n      <td>63460</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737418544,"execution_millis":54,"deepnote_table_state":{"sortBy":[],"filters":[],"pageSize":10,"pageIndex":0},"deepnote_table_loading":false,"deepnote_to_be_reexecuted":false,"cell_id":"cc5c6398b80c435a8e7a850f917df2fe","deepnote_cell_type":"code"},"source":"exog_x_tra = exog_x.loc[\"2000-01-01\":\"2015-01-01\", :]\nexog_x_tra","block_group":"5596077b022a4db29e0ea72397656d81","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":37,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":3,"row_count":16,"columns":[{"name":"CO2  Emissions","dtype":"float64","stats":{"unique_count":16,"nan_count":0,"min":"15.56001544","max":"20.46979674","histogram":[{"bin_start":15.56001544,"bin_end":16.05099357,"count":3},{"bin_start":16.05099357,"bin_end":16.5419717,"count":1},{"bin_start":16.5419717,"bin_end":17.03294983,"count":2},{"bin_start":17.03294983,"bin_end":17.52392796,"count":1},{"bin_start":17.52392796,"bin_end":18.01490609,"count":0},{"bin_start":18.01490609,"bin_end":18.50588422,"count":1},{"bin_start":18.50588422,"bin_end":18.99686235,"count":1},{"bin_start":18.99686235,"bin_end":19.48784048,"count":3},{"bin_start":19.48784048,"bin_end":19.97881861,"count":2},{"bin_start":19.97881861,"bin_end":20.46979674,"count":2}]}},{"name":"GDP","dtype":"float64","stats":{"unique_count":16,"nan_count":0,"min":"50155.7","max":"58427.6","histogram":[{"bin_start":50155.7,"bin_end":50982.89,"count":3},{"bin_start":50982.89,"bin_end":51810.079999999994,"count":1},{"bin_start":51810.079999999994,"bin_end":52637.27,"count":0},{"bin_start":52637.27,"bin_end":53464.46,"count":1},{"bin_start":53464.46,"bin_end":54291.649999999994,"count":1},{"bin_start":54291.649999999994,"bin_end":55118.84,"count":3},{"bin_start":55118.84,"bin_end":55946.03,"count":4},{"bin_start":55946.03,"bin_end":56773.22,"count":1},{"bin_start":56773.22,"bin_end":57600.409999999996,"count":1},{"bin_start":57600.409999999996,"bin_end":58427.6,"count":1}]}},{"name":"GNI Per Capita (US $)","dtype":"int64","stats":{"unique_count":16,"nan_count":0,"min":"33680","max":"55800","histogram":[{"bin_start":33680,"bin_end":35892,"count":1},{"bin_start":35892,"bin_end":38104,"count":3},{"bin_start":38104,"bin_end":40316,"count":1},{"bin_start":40316,"bin_end":42528,"count":0},{"bin_start":42528,"bin_end":44740,"count":1},{"bin_start":44740,"bin_end":46952,"count":1},{"bin_start":46952,"bin_end":49164,"count":5},{"bin_start":49164,"bin_end":51376,"count":1},{"bin_start":51376,"bin_end":53588,"count":1},{"bin_start":53588,"bin_end":55800,"count":2}]}},{"name":"_deepnote_index_column","dtype":"datetime64[ns]"}],"rows":[{"CO2  Emissions":20.46979674,"GDP":50175.7,"GNI Per Capita (US $)":33680,"_deepnote_index_column":"2000-01-01 00:00:00"},{"CO2  Emissions":20.17153693,"GDP":50155.7,"GNI Per Capita (US $)":35960,"_deepnote_index_column":"2001-01-01 00:00:00"},{"CO2  Emissions":19.44553028,"GDP":50535.3,"GNI Per Capita (US $)":36700,"_deepnote_index_column":"2002-01-01 00:00:00"},{"CO2  Emissions":19.50650553,"GDP":51503.8,"GNI Per Capita (US $)":37310,"_deepnote_index_column":"2003-01-01 00:00:00"},{"CO2  Emissions":19.59761671,"GDP":52995.2,"GNI Per Capita (US $)":39750,"_deepnote_index_column":"2004-01-01 00:00:00"},{"CO2  Emissions":19.46927252,"GDP":54338,"GNI Per Capita (US $)":43510,"_deepnote_index_column":"2005-01-01 00:00:00"},{"CO2  Emissions":18.94591684,"GDP":55314.2,"GNI Per Capita (US $)":46180,"_deepnote_index_column":"2006-01-01 00:00:00"},{"CO2  Emissions":19.04291178,"GDP":55892.2,"GNI Per Capita (US $)":47830,"_deepnote_index_column":"2007-01-01 00:00:00"},{"CO2  Emissions":18.27849126,"GDP":55433.7,"GNI Per Capita (US $)":48500,"_deepnote_index_column":"2008-01-01 00:00:00"},{"CO2  Emissions":16.80868142,"GDP":53521.2,"GNI Per Capita (US $)":49100,"_deepnote_index_column":"2009-01-01 00:00:00"}]},"text/plain":"            CO2  Emissions      GDP  GNI Per Capita (US $)\nyear                                                      \n2000-01-01       20.469797  50175.7                  33680\n2001-01-01       20.171537  50155.7                  35960\n2002-01-01       19.445530  50535.3                  36700\n2003-01-01       19.506506  51503.8                  37310\n2004-01-01       19.597617  52995.2                  39750\n2005-01-01       19.469273  54338.0                  43510\n2006-01-01       18.945917  55314.2                  46180\n2007-01-01       19.042912  55892.2                  47830\n2008-01-01       18.278491  55433.7                  48500\n2009-01-01       16.808681  53521.2                  49100\n2010-01-01       17.431737  54516.9                  47870\n2011-01-01       16.604190  54960.9                  49150\n2012-01-01       15.789760  55803.5                  50620\n2013-01-01       16.111175  56438.9                  52790\n2014-01-01       16.040917  57308.3                  53990\n2015-01-01       15.560015  58427.6                  55800","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CO2  Emissions</th>\n      <th>GDP</th>\n      <th>GNI Per Capita (US $)</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2000-01-01</th>\n      <td>20.469797</td>\n      <td>50175.7</td>\n      <td>33680</td>\n    </tr>\n    <tr>\n      <th>2001-01-01</th>\n      <td>20.171537</td>\n      <td>50155.7</td>\n      <td>35960</td>\n    </tr>\n    <tr>\n      <th>2002-01-01</th>\n      <td>19.445530</td>\n      <td>50535.3</td>\n      <td>36700</td>\n    </tr>\n    <tr>\n      <th>2003-01-01</th>\n      <td>19.506506</td>\n      <td>51503.8</td>\n      <td>37310</td>\n    </tr>\n    <tr>\n      <th>2004-01-01</th>\n      <td>19.597617</td>\n      <td>52995.2</td>\n      <td>39750</td>\n    </tr>\n    <tr>\n      <th>2005-01-01</th>\n      <td>19.469273</td>\n      <td>54338.0</td>\n      <td>43510</td>\n    </tr>\n    <tr>\n      <th>2006-01-01</th>\n      <td>18.945917</td>\n      <td>55314.2</td>\n      <td>46180</td>\n    </tr>\n    <tr>\n      <th>2007-01-01</th>\n      <td>19.042912</td>\n      <td>55892.2</td>\n      <td>47830</td>\n    </tr>\n    <tr>\n      <th>2008-01-01</th>\n      <td>18.278491</td>\n      <td>55433.7</td>\n      <td>48500</td>\n    </tr>\n    <tr>\n      <th>2009-01-01</th>\n      <td>16.808681</td>\n      <td>53521.2</td>\n      <td>49100</td>\n    </tr>\n    <tr>\n      <th>2010-01-01</th>\n      <td>17.431737</td>\n      <td>54516.9</td>\n      <td>47870</td>\n    </tr>\n    <tr>\n      <th>2011-01-01</th>\n      <td>16.604190</td>\n      <td>54960.9</td>\n      <td>49150</td>\n    </tr>\n    <tr>\n      <th>2012-01-01</th>\n      <td>15.789760</td>\n      <td>55803.5</td>\n      <td>50620</td>\n    </tr>\n    <tr>\n      <th>2013-01-01</th>\n      <td>16.111175</td>\n      <td>56438.9</td>\n      <td>52790</td>\n    </tr>\n    <tr>\n      <th>2014-01-01</th>\n      <td>16.040917</td>\n      <td>57308.3</td>\n      <td>53990</td>\n    </tr>\n    <tr>\n      <th>2015-01-01</th>\n      <td>15.560015</td>\n      <td>58427.6</td>\n      <td>55800</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737425370,"execution_millis":171,"deepnote_table_state":{"sortBy":[],"filters":[],"pageSize":10,"pageIndex":0},"deepnote_table_loading":false,"deepnote_to_be_reexecuted":false,"cell_id":"dffe5225bcc14e3085e8b11a4b2a1df4","deepnote_cell_type":"code"},"source":"exog_x_test = exog_x.loc[\"2016-01-01\":\"2019-01-01\", :]\nexog_x_test","block_group":"c4c9b7d318d049c4b74eb0c96c68a744","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":38,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":3,"row_count":4,"columns":[{"name":"CO2  Emissions","dtype":"float64","stats":{"unique_count":4,"nan_count":0,"min":"14.67338071","max":"15.2225181","histogram":[{"bin_start":14.67338071,"bin_end":14.728294449,"count":1},{"bin_start":14.728294449,"bin_end":14.783208188,"count":0},{"bin_start":14.783208188,"bin_end":14.838121927,"count":1},{"bin_start":14.838121927,"bin_end":14.893035666,"count":0},{"bin_start":14.893035666,"bin_end":14.947949405,"count":0},{"bin_start":14.947949405,"bin_end":15.002863144,"count":0},{"bin_start":15.002863144,"bin_end":15.057776883,"count":0},{"bin_start":15.057776883,"bin_end":15.112690622,"count":0},{"bin_start":15.112690622,"bin_end":15.167604361,"count":1},{"bin_start":15.167604361,"bin_end":15.2225181,"count":1}]}},{"name":"GDP","dtype":"float64","stats":{"unique_count":4,"nan_count":0,"min":"58972.9","max":"62478.3","histogram":[{"bin_start":58972.9,"bin_end":59323.44,"count":1},{"bin_start":59323.44,"bin_end":59673.98,"count":0},{"bin_start":59673.98,"bin_end":60024.520000000004,"count":1},{"bin_start":60024.520000000004,"bin_end":60375.060000000005,"count":0},{"bin_start":60375.060000000005,"bin_end":60725.600000000006,"count":0},{"bin_start":60725.600000000006,"bin_end":61076.14,"count":0},{"bin_start":61076.14,"bin_end":61426.68,"count":1},{"bin_start":61426.68,"bin_end":61777.22,"count":0},{"bin_start":61777.22,"bin_end":62127.76,"count":0},{"bin_start":62127.76,"bin_end":62478.3,"count":1}]}},{"name":"GNI Per Capita (US $)","dtype":"int64","stats":{"unique_count":4,"nan_count":0,"min":"56620","max":"63460","histogram":[{"bin_start":56620,"bin_end":57304,"count":2},{"bin_start":57304,"bin_end":57988,"count":0},{"bin_start":57988,"bin_end":58672,"count":0},{"bin_start":58672,"bin_end":59356,"count":1},{"bin_start":59356,"bin_end":60040,"count":0},{"bin_start":60040,"bin_end":60724,"count":0},{"bin_start":60724,"bin_end":61408,"count":0},{"bin_start":61408,"bin_end":62092,"count":0},{"bin_start":62092,"bin_end":62776,"count":0},{"bin_start":62776,"bin_end":63460,"count":1}]}},{"name":"_deepnote_index_column","dtype":"datetime64[ns]"}],"rows":[{"CO2  Emissions":15.14988272,"GDP":58972.9,"GNI Per Capita (US $)":56620,"_deepnote_index_column":"2016-01-01 00:00:00"},{"CO2  Emissions":14.82324544,"GDP":59914.8,"GNI Per Capita (US $)":57140,"_deepnote_index_column":"2017-01-01 00:00:00"},{"CO2  Emissions":15.2225181,"GDP":61355.6,"GNI Per Capita (US $)":59220,"_deepnote_index_column":"2018-01-01 00:00:00"},{"CO2  Emissions":14.67338071,"GDP":62478.3,"GNI Per Capita (US $)":63460,"_deepnote_index_column":"2019-01-01 00:00:00"}]},"text/plain":"            CO2  Emissions      GDP  GNI Per Capita (US $)\nyear                                                      \n2016-01-01       15.149883  58972.9                  56620\n2017-01-01       14.823245  59914.8                  57140\n2018-01-01       15.222518  61355.6                  59220\n2019-01-01       14.673381  62478.3                  63460","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CO2  Emissions</th>\n      <th>GDP</th>\n      <th>GNI Per Capita (US $)</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2016-01-01</th>\n      <td>15.149883</td>\n      <td>58972.9</td>\n      <td>56620</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>14.823245</td>\n      <td>59914.8</td>\n      <td>57140</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>15.222518</td>\n      <td>61355.6</td>\n      <td>59220</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>14.673381</td>\n      <td>62478.3</td>\n      <td>63460</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737364626,"execution_millis":9,"deepnote_table_state":{"sortBy":[],"filters":[],"pageSize":10,"pageIndex":3},"deepnote_table_loading":false,"deepnote_to_be_reexecuted":false,"cell_id":"db199e4f333d4704ae4394cffae7389f","deepnote_cell_type":"code"},"source":"f_amx_test = f_group_am.loc[\"2016-01-01\":\"2019-01-01\", :]\nf_amx_test","block_group":"8eea714324d24ab5a698b78b013e0c27","execution_count":null,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":1,"row_count":4,"columns":[{"name":"lost_crop_yield","dtype":"float64","stats":{"unique_count":4,"nan_count":0,"min":"13401.725000000002","max":"16560.09","histogram":[{"bin_start":13401.725000000002,"bin_end":13717.561500000002,"count":1},{"bin_start":13717.561500000002,"bin_end":14033.398000000001,"count":0},{"bin_start":14033.398000000001,"bin_end":14349.234500000002,"count":0},{"bin_start":14349.234500000002,"bin_end":14665.071000000002,"count":1},{"bin_start":14665.071000000002,"bin_end":14980.907500000001,"count":0},{"bin_start":14980.907500000001,"bin_end":15296.744,"count":1},{"bin_start":15296.744,"bin_end":15612.5805,"count":0},{"bin_start":15612.5805,"bin_end":15928.417000000001,"count":0},{"bin_start":15928.417000000001,"bin_end":16244.2535,"count":0},{"bin_start":16244.2535,"bin_end":16560.09,"count":1}]}},{"name":"_deepnote_index_column","dtype":"datetime64[ns]"}],"rows":[{"lost_crop_yield":15125.86,"_deepnote_index_column":"2016-01-01 00:00:00"},{"lost_crop_yield":14358.765000000001,"_deepnote_index_column":"2017-01-01 00:00:00"},{"lost_crop_yield":13401.725000000002,"_deepnote_index_column":"2018-01-01 00:00:00"},{"lost_crop_yield":16560.09,"_deepnote_index_column":"2019-01-01 00:00:00"}]},"text/plain":"            lost_crop_yield\nyear                       \n2016-01-01        15125.860\n2017-01-01        14358.765\n2018-01-01        13401.725\n2019-01-01        16560.090","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>lost_crop_yield</th>\n    </tr>\n    <tr>\n      <th>year</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2016-01-01</th>\n      <td>15125.860</td>\n    </tr>\n    <tr>\n      <th>2017-01-01</th>\n      <td>14358.765</td>\n    </tr>\n    <tr>\n      <th>2018-01-01</th>\n      <td>13401.725</td>\n    </tr>\n    <tr>\n      <th>2019-01-01</th>\n      <td>16560.090</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737666083,"execution_millis":784,"deepnote_to_be_reexecuted":false,"cell_id":"0d6a3ee2274741ca89e26afc3d614880","deepnote_cell_type":"code"},"source":"model_x = SARIMAX(f_amx, order=(2, 1, 2), exog=exog_x_tra[[\"GDP\", \"GNI Per Capita (US $)\", \"CO2  Emissions\"]], enforce_invertibility=False, enforce_stationarity=False)\nresults_x = model_x.fit()\n\n# Forecast future periods (change 'steps' to the number of periods you want to forecast)\nforecast_x = results_x.get_forecast(steps=4, exog=exog_x_test[[\"GDP\", \"GNI Per Capita (US $)\", \"CO2  Emissions\"]])  # For example, forecasting 10 steps ahead\n\n# Extract confidence intervals for the forecast\nconfidence_intervals_x = forecast_x.conf_int()\n\n# Plot the forecasted values\nplt.figure(figsize=(10, 6))\nplt.plot(f_amx.index, f_amx.values, label='Observed')  # Assuming 'data' is a pandas Series or DataFrame\n\n# Plot the forecasted values\nplt.plot(forecast_x.predicted_mean.index, forecast_x.predicted_mean.values, color='red', label='Forecast')\nplt.plot(f_amx_test, label='actual')\n\n# Shade the confidence interval\nplt.fill_between(\n    confidence_intervals_x.index,\n    confidence_intervals_x.iloc[:, 0],\n    confidence_intervals_x.iloc[:, 1],\n    color='pink',  # Adjust the color as needed\n    alpha=0.3  # Adjust transparency if desired\n)\n\nplt.title('ARIMAX Forecast with Confidence Intervals')\nplt.xlabel('Time')\nplt.ylabel('Values')\nplt.legend()\nplt.show()\n\nprint(\"RMSE: \", np.mean((forecast_x.predicted_mean.values - f_amx_test[\"lost_crop_yield\"])**2)**.5 )","block_group":"bf100a3bbfb04558821e0da9326c43a0","execution_count":null,"outputs":[{"name":"stderr","text":"/root/venv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n  self._init_dates(dates, freq)\n/root/venv/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n  self._init_dates(dates, freq)\n This problem is unconstrained.\nRUNNING THE L-BFGS-B CODE\n\n           * * *\n\nMachine precision = 2.220D-16\n N =            8     M =           10\n\nAt X0         0 variables are exactly at the bounds\n\nAt iterate    0    f=  5.90094D+00    |proj g|=  7.03763D-01\n\nAt iterate    5    f=  5.87448D+00    |proj g|=  1.03338D-01\n\nAt iterate   10    f=  5.87252D+00    |proj g|=  8.19434D-02\n\nAt iterate   15    f=  5.84763D+00    |proj g|=  2.71012D-01\n\nAt iterate   20    f=  5.82192D+00    |proj g|=  6.81501D-02\n\nAt iterate   25    f=  5.82038D+00    |proj g|=  4.40665D-03\n\n           * * *\n\nTit   = total number of iterations\nTnf   = total number of function evaluations\nTnint = total number of segments explored during Cauchy searches\nSkip  = number of BFGS updates skipped\nNact  = number of active bounds at final generalized Cauchy point\nProjg = norm of the final projected gradient\nF     = final function value\n\n           * * *\n\n   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n    8     29     39      1     0     0   5.378D-05   5.820D+00\n  F =   5.8203786718294666     \n\nCONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n","output_type":"stream"},{"data":{"text/plain":"<Figure size 1000x600 with 1 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\n"},"metadata":{},"output_type":"display_data"},{"name":"stdout","text":"RMSE:  1529.5240241009517\n","output_type":"stream"}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1701737682280,"execution_millis":19,"deepnote_to_be_reexecuted":false,"cell_id":"daa1af728a2d480aa5fd46c710fb03c7","deepnote_cell_type":"code"},"source":"from sklearn.metrics import mean_absolute_error, mean_squared_error\nimport math\n\ny_true = f_amx_test.values\ny_pred = forecast_x.predicted_mean.values\n\n# report performance\nmse = mean_squared_error(y_true, y_pred)\nprint('MSE: '+str(mse))\nmae = mean_absolute_error(y_true, y_pred)\nprint('MAE: '+str(mae))\nrmse = math.sqrt(mean_squared_error(y_true, y_pred))\nprint('RMSE: '+str(rmse))\nmape = np.mean(np.abs(y_pred - y_true)/np.abs(y_true))\nprint('MAPE: '+str(mape))","block_group":"284d4594334346118df5a2793c7073e0","execution_count":null,"outputs":[{"name":"stdout","text":"MSE: 2339443.740301969\nMAE: 1293.4475868633795\nRMSE: 1529.5240241009517\nMAPE: 0.07956202482648135\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=342fa5dc-5dd2-4212-ac75-4267e7cc0cc3' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"deepnote":{},"orig_nbformat":2,"deepnote_notebook_id":"b822e11208b2448bbf62cc0fc3bd4f6d","deepnote_execution_queue":[]}}