Editing AI for Time Series and Forecasting (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert time series evaluation uses multiple metrics and rigorous experimental design:

'''Regression metrics''': MAE (Mean Absolute Error), RMSE, MAPE (Mean Absolute Percentage Error), sMAPE. MAPE is undefined when actual=0 and is skewed by near-zero values; sMAPE or MAE are more robust.

'''Probabilistic metrics''': For probabilistic forecasts (quantile or interval), use CRPS (Continuous Ranked Probability Score) or Winkler score. These reward well-calibrated uncertainty.

'''Rolling window backtesting''': Instead of one train/test split, slide a window across history — train on windows [0:T], [0:T+1], … and evaluate on each subsequent step. This tests the model across many historical regimes and avoids cherry-picking a favorable test period.

'''Naive benchmarks''': Always compare to: naive (last value), seasonal naive (same period last cycle), and exponential smoothing. If a complex deep learning model cannot beat seasonal naive, it's not adding value.

Expert practitioners report backtesting results as distributions (mean ± std across windows) rather than a single number, and explicitly test for robustness during unusual periods (holidays, pandemics, market crashes).
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