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estimate-online-covariance

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更新日2026年6月5日 13:01

Estimate a covariance / correlation / precision matrix incrementally with precise. Use when data arrives as a stream and you want the matrix updated per observation, or when you want an online (partial_fit) drop-in for sklearn.covariance, which is batch-only.

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microprediction

microprediction/precise

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Estimate online covariance with precise

precise provides sklearn-style estimators with a single partial_fit contract. Pure numpy.

pip install precise

The pattern

import numpy as np
from precise import EwaCovariance        # exponentially weighted; recency-biased

est = EwaCovariance(r=0.05)              # r in (0,1]; larger = faster forgetting
for y in stream:                         # y is a 1-D array (one observation)
    est.partial_fit(y)

est.covariance_     # (d, d) ndarray, symmetric and PSD by construction
est.correlation_    # unit-diagonal correlation
est.precision_      # inverse covariance (when well-conditioned)
est.location_       # running mean
est.n_samples_      # observations seen

fit(X) is the batch drop-in (X is 2-D, rows = observations); it resets then replays rows, so it matches sklearn.covariance's call shape.

Choosing the class

all_estimators() lists every estimator; estimator_from_name("LedoitWolfCovariance") looks one up. Sensible defaults by situation:

general / recency-weighted: EwaCovariance(r=...)
many variables relative to samples (p/n large) or ill-conditioned: LedoitWolfCovariance, OASCovariance, ShrunkCovariance, FactorCovariance
heavy tails / outliers: HuberCovariance, TylerCovariance
regime changes: AdaptiveEwaCovariance, DCCCovariance
you don't know: use the choose-covariance-estimator skill (suggest(X)).

Notes

All estimators are truly online — constant work per observation, no growing buffers.
State is a JSON-able dict: est.get_state() / est.set_state(s) for mid-stream checkpointing.
covariance_ is always symmetric PSD; don't hand-symmetrize or clip it yourself.
Named series with a changing universe? Use the keyed-dynamic-universe skill instead.

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Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.

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Pick which precise covariance estimator to use for a given dataset. Use when you have data X and are unsure which estimator fits its dimension, conditioning, or tail behavior. Wraps precise.suggest() and covariance_features().

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Maintain an online covariance over named series whose set changes over time (e.g. assets entering and leaving). Use when observations arrive as dicts keyed by name rather than fixed-length vectors. Wraps precise's keyed / FixedUniverse / DynamicUniverse adapters.

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name	estimate-online-covariance
description	Estimate a covariance / correlation / precision matrix incrementally with precise. Use when data arrives as a stream and you want the matrix updated per observation, or when you want an online (partial_fit) drop-in for sklearn.covariance, which is batch-only.

Estimate online covariance with precise

precise provides sklearn-style estimators with a single partial_fit contract. Pure numpy.

pip install precise

The pattern

import numpy as np
from precise import EwaCovariance        # exponentially weighted; recency-biased

est = EwaCovariance(r=0.05)              # r in (0,1]; larger = faster forgetting
for y in stream:                         # y is a 1-D array (one observation)
    est.partial_fit(y)

est.covariance_     # (d, d) ndarray, symmetric and PSD by construction
est.correlation_    # unit-diagonal correlation
est.precision_      # inverse covariance (when well-conditioned)
est.location_       # running mean
est.n_samples_      # observations seen

fit(X) is the batch drop-in (X is 2-D, rows = observations); it resets then replays rows, so it matches sklearn.covariance's call shape.

Choosing the class

all_estimators() lists every estimator; estimator_from_name("LedoitWolfCovariance") looks one up. Sensible defaults by situation:

general / recency-weighted: EwaCovariance(r=...)
many variables relative to samples (p/n large) or ill-conditioned: LedoitWolfCovariance, OASCovariance, ShrunkCovariance, FactorCovariance
heavy tails / outliers: HuberCovariance, TylerCovariance
regime changes: AdaptiveEwaCovariance, DCCCovariance
you don't know: use the choose-covariance-estimator skill (suggest(X)).

Notes

All estimators are truly online — constant work per observation, no growing buffers.
State is a JSON-able dict: est.get_state() / est.set_state(s) for mid-stream checkpointing.
covariance_ is always symmetric PSD; don't hand-symmetrize or clip it yourself.
Named series with a changing universe? Use the keyed-dynamic-universe skill instead.