Master time series analysis with this ultimate guide to stationarity and non-stationarity. Explore core concepts like strict vs. weak stationarity, unit root tests (ADF/PP), trend non-stationarity, random walks, and structural breaks. Learn why stationarity is essential for reliable time series forecasting and ARIMA modeling, alongside practical methods to transform non-stationary data using differencing and log transformations. A time series is said to be stationary if its statistical properties (i) mean, (ii) variance, and (iii) autocovariance remain constant over time. Think of it like a river that always flows at the same average depth, with similar fluctuations every season.