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