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.
A random train-test split ruins a forecasting model by leaking future observations into training. Use time-based holdouts or rolling-origin backtesting, and build every feature only from data available before the forecast timestamp. Use this time series answer to show the decision, trade-off, and evidence rather than a memorised definition.
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.