Timeseries Forecasting with Gaussian process regression

Objective: Demand Forecasting

Python packages:
Python-dateutil
Pytest
Isoweek
Sklearn
Pandas
Numpy
Datetime

Steps:

1. Data processing
2. Seasonality, Trend & Residue extraction
3. Residue modelling
4. Residue prediction
5. Unit test and integration test results

Scripts information:

• timeseries_modeling_analytics.py
  Arguments:
  sample.csv: sample data csv file
  n: required forecast period for step-2 and step-4
  Trigger command: python portcast_analytics.py sample.csv 6

• test_cases.py
  Arguments: None
  Trigger command: pytest test_portcast.py