Why Stationarity Matters¶
Most traditional time series forecasting models mathematically require the data to be stationary.
The Core Concept¶
If a time series is not stationary, its statistical properties—like mean and variance—change over time. If the basic rules of the game keep changing, how can an algorithm predict the future?
A non-stationary series: * Trends upwards or downwards. * Has wilder swings (higher variance) in summer than in winter. * Has seasonal patterns that aren't constant.
A stationary series: * Idles roughly around a zero mean. * Has a constant variance (the wiggles are the same size everywhere).
By differencing (subtracting today's value from yesterday's), we remove the trend and often make the series stationary. We then forecast the differences and convert them back into real values.
KSB Mapping¶
| KSB | Description | How This Addresses It |
|---|---|---|
| K4.1 | Statistical models and methods | ARIMA, SARIMA, and exponential smoothing foundations |
| K4.2 | Predictive analytics and ML techniques | Time series forecasting and model comparison |
| K5.3 | Common patterns in real-world data | Identifying trends, seasonality, and stationarity |
| S1 | Scientific methods and hypothesis testing | Stationarity testing, model diagnostics, forecast validation |
| S4 | Analysis and models to inform outcomes | Building forecasts to support business planning |
| B5 | Impartial, hypothesis-driven approach | Honest evaluation of forecast accuracy and limitations |