#arima

1 articles

01 ·Time Series·FREE·9 min read Time-Series Forecasting from Scratch — From Classical Methods to Foundation Models What makes forecasting different from ordinary regression is that the rows are not independent and the test set is always in the future. Autocorrelation and stationarity first, then the intuition behind ARIMA, seasonal and trend decomposition, and where deep and foundation models actually sit. The last third is evaluation — no random splits, how to backtest, and why MAPE lies.