06 Stationarity in Time Series (ARIMA Modelling) (Part of Time Series Analysis & Forecasting)

# Stationarity for Time Series Analysis | ARIMA Model Series | Excel & Python Tutorial

In this session of the Time Series Analysis Series, we explore one of the most important concepts in forecasting and ARIMA modeling: **Stationarity**.

A time series must be stationary before ARIMA models can be applied effectively. In this video, you'll learn how to identify non-stationary data, test for stationarity using Excel, understand the Augmented Dickey-Fuller (ADF) test, and transform data into a stationary series using differencing.

### What You'll Learn

✅ What stationarity means in time series analysis

✅ Why stationarity is critical for AR, MA, and ARIMA models

✅ How to visually identify trends and non-stationarity in Excel

✅ Simple mean and variance tests for stationarity

✅ Understanding the Augmented Dickey-Fuller (ADF) Test

✅ Running ADF-style regression in Excel

✅ Using LINEST to calculate the ADF test statistic

✅ What differencing is and why it works

✅ First-order and higher-order differencing

✅ Preparing data for ARIMA modeling

✅ Automating stationarity testing using Python and Google Colab

### Topics Covered

* Stationary vs Non-Stationary Time Series
* Constant Mean, Variance & Autocovariance
* Visual Inspection Techniques
* Mean Split Test
* Variance Comparison
* Unit Root Concept
* Augmented Dickey-Fuller (ADF) Test
* LINEST Regression Method
* Differencing Techniques
* ARIMA Integrated Component (d)
* Python Automation for Stationarity Testing

### Who Should Watch?
* Data Analysts
* Business Analysts
* Financial Analysts
* Quantitative Researchers
* Students of Statistics & Econometrics
* Anyone learning Forecasting and Time Series Analysis

### Software Used

* Microsoft Excel
* Google Colab
* Python
* ARIMA Modeling Framework

### Next Session

Building the AR (Auto-Regressive) and MA (Moving Average) components of the ARIMA Model.

🎓 Presented by **Prof. V. Ravichandran**
🏔️ **The Mountain Path Academy**
📈 Time Series Analysis & Forecasting Series

#TimeSeriesAnalysis #Stationarity #ARIMA #Forecasting #ADFTest #ExcelTutorial #PythonForDataAnalysis #Econometrics #DataScience #Statistics #MachineLearning #BusinessAnalytics #FinancialModeling #TheMountainPathAcademy

Видео 06 Stationarity in Time Series (ARIMA Modelling) (Part of Time Series Analysis & Forecasting) канала The Mountain Path