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bindings for anofox-forecast time series forecasting library","maintainers":[{"name":"sipemu","email":"sm@data-zoo.de"}],"readme":"# @sipemu/anofox-forecast\n\nWebAssembly bindings for the [anofox-forecast](https://crates.io/crates/anofox-forecast) time series forecasting library.\n\n40+ forecasting models, automatic model selection, probabilistic postprocessing, and more — running entirely in the browser via WebAssembly.\n\n## Installation\n\n```bash\nnpm install @sipemu/anofox-forecast\n```\n\n## Quick Start\n\n```javascript\nimport init, { TimeSeries, AutoForecaster, NaiveForecaster } from '@sipemu/anofox-forecast';\n\nawait init();\n\nconst ts = new TimeSeries(new Float64Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]));\nconst model = new AutoForecaster();\nmodel.fit(ts);\nconst forecast = model.predict(5);\nconsole.log(forecast.values); // Float64Array with 5 predictions\n```\n\n## Available Models\n\n### Auto Selection\n- `AutoForecaster` — selects best model across ARIMA, ETS, and Theta families\n- `AutoEnsembleForecaster` — automatic ensemble of top-K models\n- `AutoARIMAForecaster` — automatic ARIMA order selection\n- `AutoETSForecaster` — automatic ETS model selection\n- `AutoThetaForecaster` — automatic Theta variant selection\n- `AutoTBATSForecaster` — automatic TBATS configuration\n\n### Exponential Smoothing\n- `SESForecaster` — Simple Exponential Smoothing\n- `HoltForecaster` — Holt's Linear Trend\n- `HoltWintersForecaster` — Holt-Winters (additive/multiplicative seasonality)\n- `SeasonalESForecaster` — Seasonal Exponential Smoothing\n- `ETSForecaster` — Error-Trend-Seasonal state-space framework\n\n### ARIMA\n- `ARIMAForecaster` — ARIMA(p,d,q) model\n\n### Theta\n- `ThetaForecaster` — Standard Theta\n- `OptimizedThetaForecaster` — Optimized Theta\n- `DynamicThetaForecaster` — Dynamic Theta\n\n### Baseline\n- `NaiveForecaster`, `MeanForecaster`, `SeasonalNaiveForecaster`\n- `RandomWalkWithDriftForecaster`, `SMAForecaster`, `WindowAverageForecaster`\n- `SeasonalWindowAverageForecaster`\n\n### Intermittent Demand\n- `CrostonForecaster`, `ADIDAForecaster`, `TSBForecaster`, `IMAPAForecaster`\n\n### Complex Seasonality & Volatility\n- `TBATSForecaster`, `MFLESForecaster`, `MSTLForecaster`\n- `GARCHForecaster` — volatility modeling\n\n### Ensemble\n- `EnsembleForecaster` — combine multiple models\n\n## Probabilistic Postprocessing\n\nGenerate calibrated prediction intervals using conformal prediction, historical simulation, or normal approximation.\n\n```javascript\nimport { JsConformalPredictor, JsPointForecasts, JsPostProcessor } from '@sipemu/anofox-forecast';\n\n// Conformal prediction intervals\nconst predictor = new JsConformalPredictor(0.9); // 90% coverage\npredictor.calibrate(forecasts, actuals);\nconst intervals = predictor.predictIntervals(newForecasts);\nconsole.log(intervals.lower);  // Float64Array\nconsole.log(intervals.upper);  // Float64Array\n\n// Unified PostProcessor API\nconst processor = JsPostProcessor.conformal(0.95);\nconst trained = processor.train(forecasts, actuals);\nconst pi = processor.predictIntervals(trained, newForecasts);\n\n// Backtesting\nconst config = new JsBacktestConfig(50, 5, 7); // window=50, step=5, horizon=7\nconst result = backtestPostProcessor(processor, forecasts, actuals, config);\nconsole.log(`Coverage: ${result.coverage}`);\n```\n\n### Available Postprocessing Methods\n- `JsConformalPredictor` — distribution-free prediction intervals (split, cross-val, jackknife+)\n- `JsNormalPredictor` — Gaussian error assumption baseline\n- `JsHistoricalSimulator` — non-parametric empirical error distribution\n- `JsPostProcessor` — unified API wrapping all methods\n- `JsBacktestConfig` / `JsBacktestResult` — rolling/expanding window backtesting\n\n## Common API Pattern\n\nAll forecasters share the same interface:\n\n```javascript\nconst model = new SomeForecaster(/* config */);\nmodel.fit(timeSeries);\n\n// Point forecasts\nconst forecast = model.predict(horizon);\nconsole.log(forecast.values);\n\n// With confidence intervals\nconst forecastCI = model.predictWithIntervals(horizon, 0.95);\nconsole.log(forecastCI.lower);\nconsole.log(forecastCI.upper);\n```\n\n## ETS Notation\n\nCreate ETS models using standard notation (A=Additive, M=Multiplicative, N=None):\n\n```javascript\nconst ets = ETSForecaster.fromNotation('AAA', 12); // ETS(A,A,A) with period 12\nets.fit(ts);\nconst forecast = ets.predict(12);\n```\n\n## Calendar Annotations\n\nAdd holidays and named regressors for models that support exogenous variables:\n\n```javascript\nconst calendar = new CalendarAnnotations();\ncalendar.addHoliday(Date.parse('2024-12-25'));\ncalendar.addRegressor('temperature', new Float64Array([20, 22, 25, ...]));\n\nts.setCalendar(calendar);\nmodel.fit(ts); // ARIMA, MFLES, etc. will use the regressors\n```\n\n## TypeScript Support\n\nFull TypeScript definitions are included. Import types directly:\n\n```typescript\nimport { TimeSeries, Forecast, AutoForecaster } from '@sipemu/anofox-forecast';\n\nconst ts: TimeSeries = new TimeSeries(new Float64Array([1, 2, 3]));\nconst forecast: Forecast = model.predict(5);\n```\n\n## Links\n\n- [Rust crate](https://crates.io/crates/anofox-forecast)\n- [API documentation](https://docs.rs/anofox-forecast)\n- [GitHub repository](https://github.com/sipemu/anofox-forecast)\n- [DuckDB extension](https://github.com/DataZooDE/anofox-forecast)\n\n## License\n\nMIT\n","readmeFilename":"README.md"}