Stationarity and seasonality, backtesting without lookahead, horizon and hierarchy, and why a random split ruins a forecasting model.
3 questions in Machine Learning.
Plot the series first, then use a decomposition and an ADF or KPSS test to separate trend from seasonality from noise. Difference or transform away a trend, use Fourier terms when several seasonal periods coexist, and treat holidays and calendar structure as features rather than outliers.
Because it puts future observations in the training set, so the model is scored on interpolating between points it has already seen rather than on predicting forward. The correct evaluation is a rolling-origin backtest, and every feature must be computable from data available strictly before the timestamp it describes.
Forecast at whichever levels have signal, then reconcile so the numbers are coherent by construction. Bottom-up preserves detail but accumulates noise, top-down is stable but allocates by an assumed proportion, and optimal reconciliation projects independent forecasts onto the set that adds up.