skaters

▶ Watch it race live in your browser

The actual JavaScript port racing arima, @bsull/augurs (ETS and MSTL), and Prophet (Stan compiled to WASM) on 150 real FRED series — every method scored on held-out log-likelihood, rolling one-step-ahead, no server. laplace keeps up with a moving average while the refit baselines grind. Start here →

Fast univariate online distributional time-series forecasting. Zero dependencies, runs in Node or the browser. This is the JavaScript port of the Python skaters package, numerically identical to it within 1e-6 (enforced on every release by a parity checker against the Python reference).

Every prediction is a full predictive distribution (a Dist), so it can be scored on log-likelihood — not just a point or an interval.

Install

npm install skaters

Quickstart

import { laplace } from "skaters";

const f = laplace(1);          // online, O(1) per step, the general-purpose default
let state = null;
for (const y of stream) {
  let dists;
  [dists, state] = f(y, state);
  const d = dists[0];
  d.mean;             // point forecast
  d.std;              // uncertainty
  d.quantile(0.975);  // 97.5th percentile
  d.logpdf(y);        // a real density — scorable on log-likelihood
  d.crps(y);          // ...and on CRPS
}

In the browser, import the hosted module directly — no build step:

<script type="module">
  import { laplace } from "https://skaters.microprediction.org/js/skaters/index.mjs";
</script>

What's exported

laplace and buildCandidates; the Dist object; transforms (difference, standardize, garch, ar, holtLinear, powerTransform, …); ensembles (precisionWeightedEnsemble, bayesianEnsemble, terminalLeafEnsemble); leaves (scaleMixtureLeaf, crpsLeaf, garchLeaf); multiscale, sticky, periodicity and covariance helpers; and spec (de)serialisers. See index.mjs for the full surface.

Notes

• laplace is the general-purpose default; its signature is laplace(k = 1, objective = "crps", sticky = true, scales = null). On price/return series with volatility clustering there is no free lunch — a dedicated GARCH-t model is the right tool there.
• The port is a faithful mirror of the Python package; the two agree to 1e-6.

Links

• Docs & benchmarks: https://skaters.microprediction.org
• Paper: Transforms All the Way Down — Automatic Online Distributional Forecasting by Conjugation
• Python package: https://pypi.org/project/skaters/
• Source: https://github.com/microprediction/skaters

MIT © Peter Cotton