DataZooDE
OrganizationStatistical timeseries forecasting in DuckDB
Categories
Indexed Skills (5)
anofox-forecast-models
Forecasting models and the `ts_forecast_by` / `ts_forecast_var_by` API surface of the anofox_forecast DuckDB extension. Covers 36 models (baseline, exponential smoothing, state-space ARIMA + Kalman, classical GARCH, Theta, multi-seasonal, intermittent-demand, distributional Laplace with three variants, panel/global GlobalETS/GlobalTheta/GlobalCroston, and multivariate VAR via ts_forecast_var_by), parameter surfaces (MAP + STRUCT), model selection guidance, and common workflow gotchas. Use when picking a model or writing `ts_forecast_by` / `ts_forecast_agg` / `ts_forecast_var_by` calls.
anofox-forecast-backtest
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.
anofox-forecast-data-prep
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`.
anofox-forecast-detection
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.
anofox-forecast-eda
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.
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