Time Series
Forecasting, trend and seasonality, horizon, anomaly detection — and validation that respects time, because a random split on temporal data is leakage.
The target is a future value of the series you already have. Features are lags and windows that end at the forecast origin, the naive forecast is the baseline, and beating it is harder than it looks.
Most series are a level, a trend, one or more seasonal cycles and calendar effects on top of noise. Model each explicitly or difference it away — and know that a model fitted to one regime is a bet that the trend continues.
One step ahead and twelve steps ahead are different problems with different errors. Direct and recursive strategies trade compounding error against training cost — and the horizon that matters is the one the decision needs.
Forecast the series, compare the actual to the forecast, and flag when the residual leaves a band. The band is a threshold with a false-alarm cost, the baseline must know about seasonality, labels are scarce, and an alert with no owner is noise.
Move the origin forward through time and score each forecast against what happened next; never shuffle. Report MAE or RMSE scaled against the naive forecast, per horizon and per segment — and treat MAPE with suspicion near zero.