@sipemu/anofox-forecast

WebAssembly bindings for anofox-forecast, a comprehensive time series forecasting library.

Installation

npm install @sipemu/anofox-forecast

Usage

Basic Example

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

// Create a time series from values
const data = [10, 12, 15, 14, 18, 20, 22, 25, 24, 28];
const ts = new TimeSeries(new Float64Array(data));

// Create and fit a forecaster
const model = new NaiveForecaster();
model.fit(ts);

// Generate predictions
const forecast = model.predict(5);
console.log('Predictions:', forecast.values);

With Timestamps

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

const values = [10, 12, 15, 14, 18];
const timestamps = [
  Date.parse('2024-01-01'),
  Date.parse('2024-01-02'),
  Date.parse('2024-01-03'),
  Date.parse('2024-01-04'),
  Date.parse('2024-01-05'),
];

const ts = TimeSeries.withTimestamps(
  new Float64Array(values),
  new Float64Array(timestamps)
);

Available Forecasters

Baseline Models

| Forecaster | Description | Parameters | |------------|-------------|------------| | NaiveForecaster | Last observation repeated | - | | MeanForecaster | Historical mean | - | | SeasonalNaiveForecaster | Same season from previous cycle | period | | RandomWalkDriftForecaster | Random walk with trend | - | | SMAForecaster | Simple Moving Average | window | | WindowAverageForecaster | Rolling window average | window_size | | SeasonalWindowAverageForecaster | Seasonal window average | period, window |

Exponential Smoothing Models

| Forecaster | Description | Parameters | |------------|-------------|------------| | SESForecaster | Simple Exponential Smoothing | alpha | | HoltForecaster | Holt Linear Trend (Double ES) | alpha, beta | | HoltWintersForecaster | Triple Exponential Smoothing | alpha, beta, gamma, period | | SeasonalESForecaster | Seasonal Exponential Smoothing | period | | ETSForecaster | ETS state-space model | error, trend, seasonal, period | | AutoETSForecaster | Automatic ETS selection | - |

Theta Models

| Forecaster | Description | Parameters | |------------|-------------|------------| | ThetaForecaster | Standard Theta method | - | | OptimizedThetaForecaster | Optimized Theta | - | | DynamicThetaForecaster | Dynamic coefficient updates | alpha | | AutoThetaForecaster | Automatic Theta selection | - |

ARIMA Models

| Forecaster | Description | Parameters | |------------|-------------|------------| | ARIMAForecaster | ARIMA | p, d, q | | SARIMAForecaster | Seasonal ARIMA | p, d, q, P, D, Q, period | | AutoARIMAForecaster | Automatic ARIMA selection | - |

Intermittent Demand Models

| Forecaster | Description | Parameters | |------------|-------------|------------| | CrostonForecaster | Croston's method | - | | TSBForecaster | Teunter-Syntetos-Babai | - | | ADIDAForecaster | Aggregate-Disaggregate approach | - | | IMAPAForecaster | Multiple Aggregation Prediction | - |

Advanced Models

| Forecaster | Description | Parameters | |------------|-------------|------------| | TBATSForecaster | TBATS (complex seasonality) | seasonal_periods[] | | AutoTBATSForecaster | Automatic TBATS | seasonal_periods[] | | MFLESForecaster | Multiple Frequency LOESS | seasonal_periods[] | | MSTLForecasterWrapper | MSTL decomposition | seasonal_periods[] | | GARCHForecaster | GARCH volatility model | p, q |

Prediction Intervals

Some models support prediction intervals:

import { NaiveForecaster } from '@sipemu/anofox-forecast';

const model = new NaiveForecaster();
model.fit(ts);

// Get forecast with 95% prediction intervals
const forecast = model.predictWithIntervals(5, 0.95);

console.log('Point predictions:', forecast.values);
console.log('Lower bound:', forecast.lower);
console.log('Upper bound:', forecast.upper);

API Reference

TimeSeries

• new TimeSeries(values: Float64Array) - Create from values
• TimeSeries.withTimestamps(values, timestamps) - Create with timestamps (ms since epoch)
• ts.length - Number of observations
• ts.values - Get values as array
• ts.isEmpty() - Check if empty
• ts.hasMissingValues() - Check for NaN values
• ts.slice(start, end) - Get a slice of the series

Forecast

• forecast.horizon - Number of predictions
• forecast.values - Point predictions
• forecast.lower - Lower prediction interval (if available)
• forecast.upper - Upper prediction interval (if available)
• forecast.hasLower() - Check if lower interval exists
• forecast.hasUpper() - Check if upper interval exists

ETS Model Specification

For ETSForecaster, use string codes:

• Error: "A" (additive) or "M" (multiplicative)
• Trend: "N" (none), "A" (additive), or "Ad" (additive damped)
• Seasonal: "N" (none), "A" (additive), or "M" (multiplicative)

// ETS(A,A,M) - Additive error, Additive trend, Multiplicative seasonal
const ets = new ETSForecaster("A", "A", "M", 12);

Standard ETS Notation

You can also use standard ETS notation (following FPP3 taxonomy):

// Create from notation string
const model = ETSForecaster.fromNotation("AAA", 12);  // Holt-Winters additive
const model = ETSForecaster.fromNotation("MAM", 12);  // Multiplicative Holt-Winters
const model = ETSForecaster.fromNotation("AAdM", 12); // Damped trend, multiplicative seasonal

Valid notation format: ErrorTrendSeasonal

• First letter: Error (A or M)
• Second letter(s): Trend (N, A, or Ad)
• Third letter: Seasonal (N, A, or M)

Model Validation

Some ETS combinations are unstable and will throw an error:

• MAA - Multiplicative error + Additive trend + Additive seasonal
• MAdA - Multiplicative error + Damped trend + Additive seasonal

You can check validity before creating:

// Check if a specification is valid
ETSForecaster.isValidSpec("A", "A", "A");  // true
ETSForecaster.isValidSpec("M", "A", "M");  // true
ETSForecaster.isValidSpec("M", "A", "A");  // false (unstable)

Browser Usage

<script type="module">
  import init, { TimeSeries, ThetaForecaster } from './anofox_forecast_js.js';

  async function main() {
    await init();

    const data = new Float64Array([10, 12, 15, 14, 18, 20, 22, 25]);
    const ts = new TimeSeries(data);

    const model = new ThetaForecaster();
    model.fit(ts);

    const forecast = model.predict(5);
    console.log('Forecast:', forecast.values);
  }

  main();
</script>

Node.js Usage

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

// Load your data
const data = [/* your time series data */];
const ts = new TimeSeries(new Float64Array(data));

// Forecast with AutoARIMA
const model = new AutoARIMAForecaster();
model.fit(ts);
const forecast = model.predict(10);

Limitations

• The parallel feature from the Rust crate is not available in WASM
• Postprocessing features (conformal prediction, IDR) are not yet exposed
• Cross-validation utilities are not yet exposed

License

MIT