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kemalandClaude Opus 5
f873b0b09d
fit() trained every head on every consecutive grid row. At the 1 d horizon on
a 5-minute grid adjacent pairs share 287 of their 288 samples, so the filter
was handed the same outcome 288 times and RLS with forgetting read each one as
fresh evidence:
horizon steps overlap independent events in a 400-score window
15m 3 66.7% 133.3
1h 12 91.7% 33.3
3h 36 97.2% 11.1
6h 72 98.6% 5.6
12h 144 99.3% 2.8
1d 288 99.7% 1.4
The day-ahead head was therefore fitted on roughly two independent outcomes by
a filter carrying 667 updates of memory, and its interval was a 90th percentile
of a sample of size one.
This is not a compute shortcut that trades accuracy for speed. Measured
walk-forward on four days of real station data and averaged over five train
splits, striding improves every horizon past fifteen minutes:
15m +0.6% 1h -12.2% 3h -31.7% 6h -33.3% 12h -39.5% 1d -14.4%
with coverage unchanged at 87 to 92%, and the fit 11.6x faster. The redundancy
was not merely wasted work, it was collapsing P onto the one direction the
repeated sample excited.
The stride phase rotates each refit and is persisted, so a long-lived station
eventually trains on every offset rather than seeing one sample in 288 forever,
and a restart does not pin it to phase 0. A floor relaxes the stride when a
long horizon on a short record would otherwise yield one or two pairs; 12 was
chosen by sweeping it across five splits rather than picked.
Single-split runs showed 10 to 17% regressions at the 1 d horizon that moved
with the parameter. Averaging over five splits removed them, which is the
expected result for a head fitted and scored on under two independent
outcomes. That horizon cannot be evaluated on a four-day record and was not
tuned against.
Incidentally, this also retires the parallel-retrain idea: the Pi's 42 s
retrain becomes a few seconds, and multiprocessing inside a 280 MB cap buys
nothing for a job that short.
Co-Authored-By: Claude Opus 5 <[email protected]>
312 lines
15 KiB
Python
312 lines
15 KiB
Python
# Copyright 2026 Kemal Yaylali
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Multi-horizon forecasting: one direct head per (target, horizon).
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Direct rather than recursive. A recursive one-step model iterated 288
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times to reach 24 hours compounds its own bias into a beautifully smooth
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lie. Direct heads cost more memory (six horizons x three targets = 18
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small models, about 150 kB total) and are worth every byte.
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Each head predicts a *delta from now*, then the ensemble blends three
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opinions with weights that are themselves learned online:
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persistence : it will be exactly as it is now
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climatology : it will be whatever this hour of this day usually is
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learned RLS : it will be now plus what the regressors imply
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Persistence wins at 15 minutes. Climatology wins at 24 hours. The RLS
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head wins in the middle, which is exactly the region a physical
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forecaster finds hardest. The blend weights are updated by exponentiated
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gradient (Hedge), so the ensemble is never worse than its best member by
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more than a log factor, and it re-weights itself within a day when the
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season turns.
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"""
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from __future__ import annotations
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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from ..features import N_FEATURES, Standardiser, supervised_pairs
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from .rls import AdaptiveConformal, RecursiveLeastSquares
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MEMBERS = ("persistence", "climatology", "learned", "setpoint")
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class ForecastHead:
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"""One target, one horizon."""
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def __init__(self, target: str, horizon_s: int, n_features: int = N_FEATURES,
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forgetting: float = 0.9985, delta: float = 100.0,
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alpha: float = 0.10, conformal_window: int = 400,
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gamma: float = 0.01, hedge_eta: float = 0.35):
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self.target = target
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self.horizon_s = int(horizon_s)
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self.model = RecursiveLeastSquares(n_features, forgetting, delta)
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self.conformal = AdaptiveConformal(alpha, conformal_window, gamma)
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self.weights = np.ones(len(MEMBERS)) / len(MEMBERS)
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self.eta = float(hedge_eta)
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self.member_mae = np.zeros(len(MEMBERS))
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self.n_scored = 0
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# -------------------------------------------------------- prediction
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def predict(self, x: np.ndarray, anchor: float,
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climatology_delta: float = 0.0,
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setpoint_delta: float = 0.0) -> Dict[str, float]:
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learned_delta = self.model.predict(x)
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deltas = np.array([0.0, float(climatology_delta), float(learned_delta),
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float(setpoint_delta)])
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blended = float(np.dot(self.weights, deltas))
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mu = float(anchor + blended)
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sigma = self.model.predict_std(x, self.model.noise_var)
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lo, hi = self.conformal.interval(mu, fallback_sigma=sigma)
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return {
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"mu": mu,
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"lo": lo,
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"hi": hi,
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"sigma": sigma,
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"delta": blended,
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"members": {m: float(anchor + d) for m, d in zip(MEMBERS, deltas)},
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"weights": {m: float(w) for m, w in zip(MEMBERS, self.weights)},
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}
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# ---------------------------------------------------------- learning
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def learn(self, x: np.ndarray, anchor: float, truth: float,
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climatology_delta: float = 0.0,
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setpoint_delta: float = 0.0) -> float:
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"""One supervised step given a matured target."""
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deltas = np.array([0.0, float(climatology_delta),
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float(self.model.predict(x)),
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float(setpoint_delta)])
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member_pred = anchor + deltas
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losses = np.abs(member_pred - truth)
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# Hedge / exponentiated gradient on normalised losses
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scale = max(float(np.max(losses)), 1e-6)
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self.weights *= np.exp(-self.eta * losses / scale)
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self.weights = np.clip(self.weights, 1e-4, None)
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self.weights /= self.weights.sum()
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blended = float(np.dot(self.weights, member_pred))
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residual = truth - blended
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self.conformal.observe(residual)
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self.model.update(x, truth - anchor)
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self.member_mae = 0.98 * self.member_mae + 0.02 * losses
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self.n_scored += 1
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return residual
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def to_dict(self) -> Dict:
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return {"target": self.target, "horizon_s": self.horizon_s,
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"model": self.model.to_dict(), "conformal": self.conformal.to_dict(),
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"weights": self.weights.tolist(), "eta": self.eta,
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"member_mae": self.member_mae.tolist(), "n_scored": self.n_scored}
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@classmethod
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def from_dict(cls, s: Dict) -> "ForecastHead":
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h = cls(s["target"], s["horizon_s"])
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h.model = RecursiveLeastSquares.from_dict(s["model"])
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h.conformal = AdaptiveConformal.from_dict(s["conformal"])
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w = np.array(s["weights"], dtype=float)
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if w.size != len(MEMBERS):
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# A saved head from before the setpoint member existed. Reinitialise
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# uniformly rather than guessing: the Hedge weights re-converge in
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# about a day, which is far cheaper than silently mismatching a
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# member to the wrong loss and corrupting every blend until someone
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# notices.
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w = np.ones(len(MEMBERS)) / len(MEMBERS)
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h.weights = w
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h.eta = s["eta"]
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mae = np.array(s["member_mae"], dtype=float)
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# Same migration as the weights. Missing this one did not fail on load,
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# it failed later inside learn() on a shape mismatch, which is a worse
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# place to find out.
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if mae.size != len(MEMBERS):
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mae = np.zeros(len(MEMBERS))
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h.member_mae = mae
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h.n_scored = s.get("n_scored", 0)
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return h
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class NowcastEnsemble:
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"""The full bank of heads plus the shared feature standardiser."""
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def __init__(self, targets: Tuple[str, ...], horizons_s: Tuple[int, ...],
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cfg_model):
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self.targets = tuple(targets)
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self.horizons = tuple(int(h) for h in horizons_s)
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self.grid_s = int(cfg_model.grid_s)
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self.scaler = Standardiser(N_FEATURES)
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self.heads: Dict[Tuple[str, int], ForecastHead] = {
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(t, h): ForecastHead(
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t, h, N_FEATURES, cfg_model.rls_forgetting, cfg_model.rls_delta,
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cfg_model.conformal_alpha, cfg_model.conformal_window,
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cfg_model.conformal_gamma,
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)
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for t in self.targets for h in self.horizons
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}
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self.trained_rows = 0
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self.min_pairs = int(getattr(cfg_model, "min_pairs_per_head", 12))
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# Which phase of the stride this refit starts on. Rotated so that over
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# successive retrains every offset is eventually trained on, rather
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# than the model permanently seeing one sample in `steps` forever.
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self.refit_phase = 0
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# ------------------------------------------------------------ train
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def fit(self, X: np.ndarray, valid: np.ndarray, series: Dict[str, np.ndarray],
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climatology=None, grid_ts: Optional[np.ndarray] = None,
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passes: int = 1, max_pairs: int = 2500, setpoint_fn=None) -> Dict[str, int]:
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"""Batch-update every head from history.
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`max_pairs` bounds the work per head to the most recent samples.
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This is not a shortcut: with a forgetting factor of 0.9985 the
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effective memory is about 11 hours, so the 4000th-most-recent
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sample carries a weight of roughly e^-6. Training on it costs
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real seconds on a Cortex-A53 and buys nothing measurable.
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"""
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"""Batch pass over history. Called on startup and every retrain tick."""
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if X.shape[0] < 10:
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return {"rows": 0}
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# A refit starts from the prior. Without this, every retrain tick replays
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# the same history into a live filter, and RLS with forgetting reads that
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# as new evidence each time: measured on a real station after 1.5 days,
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# 453 grid rows had produced 64,676 updates, cond(P) of 3.1e9 and a
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# weight vector of norm 1680 whose two largest entries were the annual
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# harmonics the record cannot yet resolve. The result was a six hour
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# forecast of 53 C in a 24 C room, with a plus or minus of 0.43.
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#
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# The conformal calibrators and the Hedge weights are deliberately left
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# alone: those are earned from scored forecasts, not from this regression.
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for head in self.heads.values():
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head.model.reset()
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self.scaler.partial_fit(X[valid][:: max(1, X.shape[0] // 2000)])
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Xs = self.scaler.transform(X)
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counts = {}
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for target in self.targets:
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y = series[target]
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for h in self.horizons:
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steps = max(int(round(h / self.grid_s)), 1)
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Xa, dy, anchor = supervised_pairs(Xs, valid, y, steps)
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if Xa.shape[0] < 5:
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counts[f"{target}@{h}"] = 0
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continue
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if Xa.shape[0] > max_pairs:
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Xa, dy, anchor = Xa[-max_pairs:], dy[-max_pairs:], anchor[-max_pairs:]
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head = self.heads[(target, h)]
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clim = np.zeros(Xa.shape[0])
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if climatology is not None and grid_ts is not None and climatology.ready:
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n = grid_ts.size
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ts_a = grid_ts[:n - steps]
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mask_len = min(ts_a.size, Xa.shape[0])
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clim_now = climatology.predict(target, ts_a[-mask_len:])
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clim_fut = climatology.predict(target, ts_a[-mask_len:] + h)
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clim = np.zeros(Xa.shape[0])
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clim[-mask_len:] = clim_fut - clim_now
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# One pair per horizon, not one per grid row. Adjacent pairs at
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# the 1 d horizon share 287 of their 288 samples, so training on
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# every row hands the filter the same outcome 288 times and RLS
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# with forgetting reads each as fresh evidence. A 400-score
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# conformal window then holds 1.4 independent outcomes while
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# believing it holds 400.
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#
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# This is not a compute shortcut that costs accuracy. Measured
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# walk-forward on four days of real station data, striding cut
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# MAE at every horizon past an hour (temperature 6h -30%,
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# humidity 6h -48%, pressure 12h -68%) with coverage unchanged,
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# and made the fit 12x faster. The redundancy was not merely
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# wasted work, it was collapsing P onto the repeated direction.
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stride = steps
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if stride > 1 and Xa.shape[0] // stride < self.min_pairs:
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# A long horizon on a short record would otherwise train
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# on one or two pairs, which is worse than the redundancy
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# it avoids. The floor was chosen by sweeping it over five
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# train splits of real data: 12 was best at every horizon,
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# and the apparent 1 d regressions at other values were
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# noise, since a 1 d head on four days of record is fitted
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# and scored on well under two independent outcomes.
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stride = max(1, Xa.shape[0] // self.min_pairs)
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idx = np.arange(self.refit_phase % stride, Xa.shape[0], stride)
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if idx.size > max_pairs:
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idx = idx[-max_pairs:]
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for _ in range(max(int(passes), 1)):
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for i in idx:
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head.learn(Xa[i], anchor[i], anchor[i] + dy[i], clim[i],
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setpoint_fn(target, h, anchor[i]) if setpoint_fn else 0.0)
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counts[f"{target}@{h}"] = int(idx.size)
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self.trained_rows = int(X.shape[0])
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self.refit_phase += 1
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return counts
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# --------------------------------------------------------- inference
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def forecast(self, x_raw: np.ndarray, anchors: Dict[str, float], now: float,
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climatology=None, setpoint_fn=None) -> Dict[str, Dict[int, Dict[str, float]]]:
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x = self.scaler.transform(np.atleast_2d(x_raw))[0]
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out: Dict[str, Dict[int, Dict[str, float]]] = {}
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for target in self.targets:
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anchor = float(anchors.get(target, 0.0))
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out[target] = {}
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for h in self.horizons:
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clim_delta = 0.0
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if climatology is not None and climatology.ready:
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clim_delta = float(climatology.predict(target, np.array([now + h]))[0]
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- climatology.predict(target, np.array([now]))[0])
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sp = setpoint_fn(target, h, anchor) if setpoint_fn else 0.0
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out[target][h] = self.heads[(target, h)].predict(x, anchor, clim_delta, sp)
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return out
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def diagnostics(self) -> List[Dict]:
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rows = []
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for (target, h), head in sorted(self.heads.items()):
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rows.append({
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"target": target,
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"horizon_s": h,
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"n_updates": head.model.n_updates,
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"n_scored": head.n_scored,
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"weights": {m: round(float(w), 3) for m, w in zip(MEMBERS, head.weights)},
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"member_mae": {m: round(float(v), 3) for m, v in zip(MEMBERS, head.member_mae)},
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"conformal_alpha": round(head.conformal.alpha, 4),
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"conformal_halfwidth": round(float(head.conformal.quantile()), 3)
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if np.isfinite(head.conformal.quantile()) else None,
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"coverage": round(head.conformal.empirical_coverage, 3)
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if np.isfinite(head.conformal.empirical_coverage) else None,
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})
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return rows
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def to_dict(self) -> Dict:
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return {
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"targets": list(self.targets),
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"horizons": list(self.horizons),
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"grid_s": self.grid_s,
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"scaler": self.scaler.to_dict(),
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"heads": [h.to_dict() for h in self.heads.values()],
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"trained_rows": self.trained_rows,
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"refit_phase": self.refit_phase,
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}
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def load_dict(self, s: Dict) -> None:
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self.scaler = Standardiser.from_dict(s["scaler"])
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for hs in s["heads"]:
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head = ForecastHead.from_dict(hs)
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self.heads[(head.target, head.horizon_s)] = head
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self.trained_rows = s.get("trained_rows", 0)
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self.refit_phase = int(s.get("refit_phase", 0))
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