skaters 0.8.0

Fast univariate time series models that run in Pyodide

skaters

Fast univariate online time series models — in Python and JavaScript. Zero dependencies. Runs natively in the browser or in Pyodide.

Python and JavaScript, verified identical. The full library is ported to zero-dependency JavaScript and checked against the Python reference to 1e-6. Use it natively in the browser or via Pyodide — see the live demos.

Install

pip install skaters

Quick start

from skaters import laplace

f = laplace(k=3)
state = None
for y in observations:
    dists, state = f(y, state)
    dists[0].mean              # point forecast
    dists[0].std               # uncertainty
    dists[0].quantile(0.975)   # 95th percentile
    dists[0].logpdf(y)         # log-likelihood
    dists[0].cdf(y)            # CDF at y

Every skater returns list[Dist] — a weighted Gaussian mixture for each horizon $h = 1, \ldots, k$. Point forecasts, uncertainty, density evaluation, and quantiles are all aspects of the same object.

The two forecasters

skaters exposes exactly two named forecasters — everything else is a building block (transforms, leaves, ensembles) you can compose. ("skater" is the concept — any (y, state) -> ([Dist], state) function; it is no longer a function name.)

from skaters import laplace, doob

f = laplace(k=1)   # general purpose — the default
f = doob(k=1)      # committed martingale + volatility clock (feed levels)

laplace — the general forecaster

A likelihood-weighted Bayesian ensemble over a large candidate population (EMA, differencing, drift, Holt, AR, fractional differencing, seasonal, a Yeo-Johnson coordinate grid, a fast/slow two-systems block, …). Three things are on by default, each a free or near-free win:

• model first, conform last — the trunk is weighted by likelihood (honest modelling); the terminal leaf is fit by CRPS (objective="crps"). On a 2,500-series FRED study this matches a CRPS specialist on CRPS and lifts likelihood on real data. Switch back with objective="likelihood".
• lattice projection (sticky=True) — near-Dirac atoms on the exact values a series revisits. Free on continuous data (it vanishes), a large win on grid/repeating series (policy rates, posted prices).
• coordinate learning — a Yeo-Johnson λ-grid lets the ensemble learn whether the series is simple in a log/multiplicative, sqrt, or linear coordinate.

f = laplace(k=1)                          # CRPS leaf + lattice, both on
f = laplace(k=1, objective="likelihood")  # pure-likelihood leaf
f = laplace(k=1, sticky=False)            # no lattice projection

doob — the martingale specialist

A committed, driftless martingale with a learned volatility clock: a Bayesian average over martingale predictives that differ only in their volatility model (constant, GARCH, slowly-varying, heavy-tailed). Because every candidate shares the same mean, plain averaging blends the clocks without washing out kurtosis. By Dambis–Dubins–Schwarz a continuous martingale is a time-changed Brownian motion — "BM on a stochastic clock".

Feed it the level series (prices, indices, rates), not pre-differenced changes. When the martingale prior holds it edges laplace by committing the mean and spending its capacity on the clock; on genuinely mean-reverting series (e.g. the VIX) the prior is wrong and it gives ground — a deliberately sharp tool.

Architecture

Everything is transforms all the way down, with a distributional leaf at the bottom:

$$y ;\xrightarrow{T_1}; y' ;\xrightarrow{T_2}; y'' ;\xrightarrow{\cdots}; \text{leaf} ;\rightarrow; \hat{D}$$

The leaf estimates $\hat{D} = \mathcal{N}(0, \hat\sigma^2)$ from residuals via Welford's algorithm. The prediction in the original space is obtained by inverting the transform chain:

$$\hat{D}_{\text{original}} = T_1^{-1}\bigl(T_2^{-1}\bigl(\cdots\bigl(\hat{D}\bigr)\bigr)\bigr)$$

Every node returns list[Dist]. There is no separate "point forecast" vs "uncertainty" — both are aspects of the same $\hat{D}$.

The key insight

Every "model" is really a transform. An EMA doesn't "predict" — it subtracts a running level $\ell_t$, leaving simpler residuals $\varepsilon_t = y_t - \ell_t$. The prediction comes from inverting the transform chain applied to the leaf's distributional estimate.

The Dist type

A weighted mixture of Gaussians $\sum_{i} w_i ,\mathcal{N}(\mu_i, \sigma_i^2)$. Pure Python (math.erf, math.exp).

from skaters import Dist

d = Dist.gaussian(5.0, 2.0)
d.mean                  # 5.0
d.std                   # 2.0
d.pdf(5.0)              # density at x
d.cdf(3.0)              # P(X <= 3)
d.logpdf(5.0)           # log-likelihood
d.quantile(0.975)       # inverse CDF

# Exact mixture combination (for ensembles)
mix = Dist.combine([d1, d2, d3], weights=[0.5, 0.3, 0.2])

# Propagate through transform inverses
d.shift(10.0)           # translate: mu -> mu + 10
d.scale(2.0)            # scale: mu -> 2*mu, sigma -> 2*sigma
d.affine(2.0, 3.0)      # x -> 2x + 3

# Bound component growth
d.prune(max_components=10)

Transforms

Online bijective maps. Each has a forward (scalar in, scalar out) and an inverse_k that propagates $\text{Dist}$ objects back through the inverse.

Transform	Forward	Inverse	Use case
ema_transform($\alpha$)	$y'_t = y_t - \ell_t$	$D \mapsto D + \ell_t$	Remove level
difference()	$y't = y_t - y{t-1}$	Cumsum with $\text{Var}$ growing as $\sum \sigma_h^2$	Random walk
drift($\alpha, \lambda$)	$y'_t = \Delta y_t - \hat\mu_t$	$y_t + h\hat\mu + \sum\varepsilon$	Random walk + drift
holt_linear($\alpha, \beta$)	$y'_t = y_t - (\ell_t + b_t)$	$\ell_t + h \cdot b_t + \varepsilon$	Level + trend (Holt 1957)
ar($p$)	$y't = y_t - \sum \hat\phi_j y{t-j}$	AR reconstruction with variance propagation	Autoregression (online RLS)
grouped_ar($L$)	Same, grouped coefficients	Same	Long-lag AR with $O(\log L)$ params
fractional_difference($d$)	$y'_t = (1-B)^d , y_t$	$(1-B)^{-d}$	Long memory
standardize($\alpha$)	$y'_t = (y_t - \hat\mu_t) / \hat\sigma_t$	$D \mapsto \hat\sigma_t \cdot D + \hat\mu_t$	Remove scale
garch($\omega, \alpha, \beta$)	$y'_t = y_t / \hat\sigma_t$	$D \mapsto \hat\sigma_t \cdot D$	Volatility clustering
seasonal_difference($s$)	$y't = y_t - y{t-s}$	Shift by lagged value	Periodicity
power_transform($p$)	$y'_t = \text{sign}(y_t)|y_t|^p$	Delta method	Tail compression

Conjugation

Transforms compose via conjugation. Given a transform $T$ and a skater $f$:

$$f_{\text{conjugated}}(y) = T^{-1}!\bigl(f\bigl(T(y)\bigr)\bigr)$$

The pipe | notation reads left-to-right (outermost transform first):

from skaters import conjugate, ema, difference, standardize

# diff removes trend, EMA predicts the differenced series
f = conjugate(ema(alpha=0.1, k=3), difference(), k=3)

# Chain: standardize, then difference, then EMA
f = conjugate(
    conjugate(ema(alpha=0.1, k=3), difference(), k=3),
    standardize(),
    k=3,
)
# canonical name: std|diff|ema_t|leaf

Ensembles

Precision-weighted (MSE)

Weights by $w_i \propto 1/\text{MSE}_i$ where $\text{MSE} = \text{bias}^2 + \text{variance}$.

from skaters import precision_weighted_ensemble, ema

f = precision_weighted_ensemble([
    ema(alpha=0.05, k=1),
    ema(alpha=0.2, k=1),
], k=1)

Bayesian (log-likelihood, XGBoost-inspired regularization)

Each model $i$ accumulates a log-weight updated at every observation:

$$\log w_i ;\mathrel{+}=; \eta \cdot \log p_i(y_t) ;-; \lambda \cdot d_i$$

where $\eta$ is the learning rate (shrinkage), $\lambda$ is the complexity penalty, and $d_i$ is the model's depth. Predictions are combined via $\text{Dist.combine}$ with softmax weights.

from skaters import bayesian_ensemble, ema

f = bayesian_ensemble(
    [ema(alpha=0.05, k=1), ema(alpha=0.2, k=1)],
    k=1,
    learning_rate=0.5,       # eta: prevents over-concentrating
    complexity_penalty=0.02, # lambda: penalizes deeper chains
    depths=[1, 1],
)

Adaptive search (beam search over transform grammar)

Grows the candidate population online: expand top performers with new transforms, replay recent history to warm-start, prune losers.

from skaters import search

f = search(
    k=1,
    expand_interval=100,  # expand top performers every 100 obs
    max_depth=3,          # maximum transform chain depth
    replay_buffer=500,    # warm-start new candidates on recent history
    max_pool=30,          # cap active candidates
)

Heavy tails: the scale-mixture leaf

Everything here is judged by predictive log-likelihood. A plain Gaussian leaf gets the location and scale right but the shape wrong on heavy-tailed residuals (returns, macro data), and — crucially — Bayesian model averaging preserves the mean and variance but washes the kurtosis out, so adding heavy leaves to the candidate pool doesn't help.

The fix is the scale-mixture leaf: a fixed dictionary of zero-mean Gaussians N(0, aᵢ·σ) with weights learned online (a Student-t is a Gaussian scale mixture, so this approximates it). It's a plain Dist; the weights are the "discrepancy from N(0,1)" — all on a=1 is Gaussian, mass on larger a is fat tails. It matches the Gaussian leaf on Gaussian data and beats it as tails fatten.

from skaters import scale_mixture_leaf, terminal_leaf_ensemble, leaf

Because mixing washes out shape, the named policies use a terminal-leaf ensemble: the candidates are combined for the mean, then one terminal scale-mixture leaf models the combined residual — so the leaf's shape reaches the output undiluted. On Student-t₃ this takes laplace from a logpdf of ≈ −2.07 (Gaussian-collapsed) to ≈ −1.93, with no cost on Gaussian data.

Dist.crps(y) (closed-form CRPS) is also available as a proper score for benchmarking.

Spec system

Serialize and rebuild any pipeline:

from skaters import (
    build, spec_name, to_json, from_json,
    ema_spec, conjugate_spec, ensemble_spec, diff_spec,
)

spec = ensemble_spec(
    conjugate_spec(ema_spec(0.1, k=1), diff_spec()),
    ema_spec(0.3, k=1),
    k=1,
)

spec_name(spec)     # "ensemble(diff|ema(0.1),ema(0.3))"
j = to_json(spec)   # JSON string
f = build(from_json(j))  # live skater

Writing a custom transform

Any $(T, T^{-1})$ pair where forward is scalar and inverse_k maps list[Dist]:

def my_transform():
    def forward(y, state):
        if state is None:
            return 0.0, {"anchor": y}
        transformed = y - state["anchor"]
        return transformed, {"anchor": y}

    def inverse_k(dists, state):
        return [d.shift(state["anchor"]) for d in dists]

    return forward, inverse_k

JavaScript & the browser

The whole library is also a zero-dependency JavaScript port (docs/js/skaters/) — every transform, ensemble, and named policy. It is verified against the Python reference by a parity suite that checks 76,000+ values to 1e-6 (parity/, run in the test suite via tests/test_js_parity.py).

<script type="module">
  import { laplace } from "https://skaters.microprediction.org/js/skaters/index.mjs";
  const f = laplace(1);
  let state = null;
  for (const y of observations) {
    const [dists, st] = f(y, state); state = st;
    dists[0].mean;            // point forecast
    dists[0].quantile(0.975); // 97.5th percentile
  }
</script>

Interactive demos (forecasting playground in native JS, and the real Python package running in Pyodide) live at skaters.microprediction.org/demos.

Design

• Online only — $O(1)$ per observation, no batch recomputation
• Distributional — every prediction is a $\text{Dist}$, not a point estimate
• Composable — transforms chain, ensembles nest, everything returns $\text{Dist}$
• Pure Python — zero dependencies, only math.erf and math.exp
• Pyodide compatible — works in the browser via WebAssembly

Lineage

This package distills ideas from timemachines, which provided a common skater interface for dozens of time series packages. This is a from-scratch rewrite focused on speed, distributional predictions, and browser compatibility.