@sipemu/anofox-forecast

WebAssembly bindings for the anofox-forecast time series forecasting library.

40+ forecasting models, automatic model selection, probabilistic postprocessing, and more — running entirely in the browser via WebAssembly.

Installation

npm install @sipemu/anofox-forecast

Quick Start

import init, { TimeSeries, AutoForecaster, NaiveForecaster } from '@sipemu/anofox-forecast';

await init();

const ts = new TimeSeries(new Float64Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]));
const model = new AutoForecaster();
model.fit(ts);
const forecast = model.predict(5);
console.log(forecast.values); // Float64Array with 5 predictions

Available Models

Auto Selection

• AutoForecaster — selects best model across ARIMA, ETS, and Theta families
• AutoEnsembleForecaster — automatic ensemble of top-K models
• AutoARIMAForecaster — automatic ARIMA order selection
• AutoETSForecaster — automatic ETS model selection
• AutoThetaForecaster — automatic Theta variant selection
• AutoTBATSForecaster — automatic TBATS configuration

Exponential Smoothing

• SESForecaster — Simple Exponential Smoothing
• HoltForecaster — Holt's Linear Trend
• HoltWintersForecaster — Holt-Winters (additive/multiplicative seasonality)
• SeasonalESForecaster — Seasonal Exponential Smoothing
• ETSForecaster — Error-Trend-Seasonal state-space framework

ARIMA

• ARIMAForecaster — ARIMA(p,d,q) model

Theta

• ThetaForecaster — Standard Theta
• OptimizedThetaForecaster — Optimized Theta
• DynamicThetaForecaster — Dynamic Theta

Baseline

• NaiveForecaster, MeanForecaster, SeasonalNaiveForecaster
• RandomWalkWithDriftForecaster, SMAForecaster, WindowAverageForecaster
• SeasonalWindowAverageForecaster

Intermittent Demand

• CrostonForecaster, ADIDAForecaster, TSBForecaster, IMAPAForecaster

Complex Seasonality & Volatility

• TBATSForecaster, MFLESForecaster, MSTLForecaster
• GARCHForecaster — volatility modeling

Ensemble

• EnsembleForecaster — combine multiple models

Probabilistic Postprocessing

Generate calibrated prediction intervals using conformal prediction, historical simulation, or normal approximation.

import { JsConformalPredictor, JsPointForecasts, JsPostProcessor } from '@sipemu/anofox-forecast';

// Conformal prediction intervals
const predictor = new JsConformalPredictor(0.9); // 90% coverage
predictor.calibrate(forecasts, actuals);
const intervals = predictor.predictIntervals(newForecasts);
console.log(intervals.lower);  // Float64Array
console.log(intervals.upper);  // Float64Array

// Unified PostProcessor API
const processor = JsPostProcessor.conformal(0.95);
const trained = processor.train(forecasts, actuals);
const pi = processor.predictIntervals(trained, newForecasts);

// Backtesting
const config = new JsBacktestConfig(50, 5, 7); // window=50, step=5, horizon=7
const result = backtestPostProcessor(processor, forecasts, actuals, config);
console.log(`Coverage: ${result.coverage}`);

Available Postprocessing Methods

• JsConformalPredictor — distribution-free prediction intervals (split, cross-val, jackknife+)
• JsNormalPredictor — Gaussian error assumption baseline
• JsHistoricalSimulator — non-parametric empirical error distribution
• JsPostProcessor — unified API wrapping all methods
• JsBacktestConfig / JsBacktestResult — rolling/expanding window backtesting

Common API Pattern

All forecasters share the same interface:

const model = new SomeForecaster(/* config */);
model.fit(timeSeries);

// Point forecasts
const forecast = model.predict(horizon);
console.log(forecast.values);

// With confidence intervals
const forecastCI = model.predictWithIntervals(horizon, 0.95);
console.log(forecastCI.lower);
console.log(forecastCI.upper);

ETS Notation

Create ETS models using standard notation (A=Additive, M=Multiplicative, N=None):

const ets = ETSForecaster.fromNotation('AAA', 12); // ETS(A,A,A) with period 12
ets.fit(ts);
const forecast = ets.predict(12);

Calendar Annotations

Add holidays and named regressors for models that support exogenous variables:

const calendar = new CalendarAnnotations();
calendar.addHoliday(Date.parse('2024-12-25'));
calendar.addRegressor('temperature', new Float64Array([20, 22, 25, ...]));

ts.setCalendar(calendar);
model.fit(ts); // ARIMA, MFLES, etc. will use the regressors

TypeScript Support

Full TypeScript definitions are included. Import types directly:

import { TimeSeries, Forecast, AutoForecaster } from '@sipemu/anofox-forecast';

const ts: TimeSeries = new TimeSeries(new Float64Array([1, 2, 3]));
const forecast: Forecast = model.predict(5);

Links

• Rust crate
• API documentation
• GitHub repository
• DuckDB extension

License

MIT