Overview
In time series analysis, a process is stationary if its statistical properties (mean, variance, autocorrelation) are constant over time. Most time series models (like ARIMA) require the data to be stationary before they can be applied.
Why Stationarity Matters
If a series is non-stationary, its behavior changes over time, making it difficult to model and forecast accurately.
Making Data Stationary
- Differencing: Subtracting the previous value from the current value.
- Transformation: Applying log or square root to stabilize variance.
- Detrending: Removing the underlying trend.