anofox-forecast

Forecasting models and the `ts_forecast_by` API surface of the anofox_forecast DuckDB extension. Covers 33 models (baseline, exponential smoothing, state-space, ARIMA, Theta, multi-seasonal, intermittent-demand, distributional Laplace with three variants), parameter surfaces (MAP + STRUCT), model selection guidance, and common workflow gotchas. Use when picking a model or writing `ts_forecast_by` / `ts_forecast_agg` calls.

Backtesting, cross-validation, evaluation metrics, and conformal prediction intervals for the anofox_forecast DuckDB extension. Use when evaluating forecast accuracy, comparing models with time-series-aware CV, computing metrics (MAE / RMSE / MAPE / MASE / coverage), or attaching distribution-free prediction intervals to forecasts.

Data preparation for the anofox_forecast DuckDB extension — filling gaps, imputing nulls, dropping bad series, differencing, detrending, hierarchical key operations. Use when preparing raw time series for downstream forecasting or backtesting with `ts_forecast_by` / `ts_cv_folds_by`.

Seasonality, changepoint, peak, and decomposition detection for the anofox_forecast DuckDB extension. Use when identifying seasonal periods before configuring seasonal forecasting models, detecting structural breaks, analysing peak timing regularity, or decomposing a series into trend / seasonal / residual components.

Exploratory data analysis and data quality for the anofox_forecast DuckDB extension — 34 per-series statistics, data-quality scoring, quality-report summaries, and 117 tsfresh-compatible feature extraction. Use before forecasting to understand series characteristics (length, gaps, trend, seasonality strength, intermittency) or to build ML feature vectors for downstream models.