diff --git a/docs/index.md b/docs/index.md
index 40789a1..d460cdd 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -61,6 +61,7 @@ The above function calculates the 50th percentile, i.e., the median of the featu
aggregate.md
wrappers.md
functions.md
+timeseries.md
```
---
diff --git a/docs/timeseries.md b/docs/timeseries.md
new file mode 100644
index 0000000..9eb9019
--- /dev/null
+++ b/docs/timeseries.md
@@ -0,0 +1,59 @@
+# Time Series Utilities
+
+
+
+Feature engineering, time series stationarity checks are few of the use-cases that are compiled in this gists. Check
+individual module defination and functionalities as follows.
+
+## Stationarity & Unit Roots
+
+Stationarity is one of the fundamental concepts in time series analysis. The
+**time series data model works on the principle that the [_data is stationary_](https://www.analyticsvidhya.com/blog/2021/04/how-to-check-stationarity-of-data-in-python/)
+and [_data has no unit roots_](https://www.analyticsvidhya.com/blog/2018/09/non-stationary-time-series-python/)**, this means:
+ * the data must have a constant mean (across all periods),
+ * the data should have a constant variance, and
+ * auto-covariance should not be dependent on time.
+
+Let's understand the concept using the following example, for more information check [this link](https://www.analyticsvidhya.com/blog/2018/09/non-stationary-time-series-python/).
+
+![Non-Stationary Time Series](https://cdn.analyticsvidhya.com/wp-content/uploads/2018/09/ns5-e1536673990684.png)
+
+
+
+| ADF Test | KPSS Test | Series Type | Additional Steps |
+| :---: | :---: | :---: | --- |
+| ✅ | ✅ | _stationary_ | |
+| ❌ | ❌ | _non-stationary_ | |
+| ✅ | ❌ | _difference-stationary_ | Use differencing to make series stationary. |
+| ❌ | ✅ | _trend-stationary_ | Remove trend to make the series _strict stationary. |
+
+
+
+```{eval-rst}
+.. automodule:: pandaswizard.timeseries.stationarity
+ :members:
+ :undoc-members:
+ :show-inheritance:
+```
+
+## Time Series Featuring
+
+Time series analysis is a special segment of AI/ML application development where a feature is dependent on time. The code here
+is desgined to create a *sequence* of `x` and `y` data needed in a time series problem. The function is defined with two input
+parameters (I) **Lootback Period (T) `n_lookback`**, and (II) **Forecast Period (H) `n_forecast`** which can be visually
+presented below.
+
+
+
+![prediction-sequence](https://i.stack.imgur.com/YXwMJ.png)
+
+
+
+```{eval-rst}
+.. automodule:: pandaswizard.timeseries.ts_featuring
+ :members:
+ :undoc-members:
+ :show-inheritance:
+```
+
+
diff --git a/pandaswizard/__init__.py b/pandaswizard/__init__.py
index 23a933c..75f5d67 100644
--- a/pandaswizard/__init__.py
+++ b/pandaswizard/__init__.py
@@ -28,3 +28,4 @@
from pandaswizard import window
from pandaswizard import wrappers
from pandaswizard import functions
+from pandaswizard import timeseries
diff --git a/pandaswizard/timeseries/__init__.py b/pandaswizard/timeseries/__init__.py
new file mode 100644
index 0000000..5f7a7bd
--- /dev/null
+++ b/pandaswizard/timeseries/__init__.py
@@ -0,0 +1,29 @@
+# -*- encoding: utf-8 -*-
+
+"""
+A Set of Utility Functions for a Time Series Data for ``pandas``
+
+A time series analysis is a way of analyzing a sequence of data which
+was collected over a period of time and the data is collected at a
+specific intervals. The :mod:`pandas` provides various functions to
+manipulate a time object which is a wrapper over the ``datetime``
+module and is a type of ``pd.Timestamp`` object.
+
+The module provides some utility functions for a time series data
+and tests like "stationarity" which is a must in EDA!
+
+.. caution::
+ The time series module consists of functions which was earlier
+ developed in `GH/sqlparser