Documentation Index
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Rolling-origin cross-validation of machine-learning forecasters: several windows, refit schedule, expanding or fixed-size training, interval coverage, accuracy metrics by model, series and window; LightGBM boosting-round search with early stopping.
forecast_backtest: 2 tools.
| Tool | What it does | Notes |
|---|---|---|
forecast_cross_validation | Rolling-origin backtest: for each of n_windows origins (step_size apart) trains the models on the history before it (all of it, or the last input_size points; retrained every refit windows) and forecasts the next h periods, then scores them against the actuals. | |
forecast_lightgbm_cv | Trains one LightGBM booster per backtest window simultaneously, eval_every rounds at a time, scoring the whole h-step recursive forecast of every window (MAPE or RMSE, weighted across windows) and stopping early when it no longer improves. |
fincept_describe_tool.