Time Series Forecasting Made Simple (Part 4.1): Understanding Stationarity in a Time Series

A time series is considered stationary when it has a constant mean, variance, and autocovariance structure over time. Stationarity can be identified through several methods, including visual inspection of plots, comparing statistics between data splits, analyzing Autocorrelation (ACF) plots, and using statistical tests like the Augmented Dickey-Fuller (ADF) and KPSS tests. Non-stationary series, which often contain trends or seasonality, can be transformed into a stationary one using a technique called differencing, which involves subtracting previous values. This process is a necessary prerequisite for many forecasting models, such as ARIMA, because they rely on stable statistical properties to make reliable predictions based on past patterns.