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Robust and classical ACF/ACOVF estimation from periodogram and M-periodogram

Project description

acfmperiod

Python port of the original R package acfMPeriod for classical and robust autocovariance/autocorrelation estimation using periodogram and M-periodogram methods.

Features

  • Univariate and multivariate ACF/ACOVF from periodogram (PerACF)
  • Robust ACF/ACOVF from M-periodogram (MPerACF)
  • Periodogram and cross-periodogram estimators
  • Covariance/correlation matrix wrappers at lag 0 (CovCorPer, CovCorMPer)
  • Plotting support for robust and classical outputs

Installation

pip install acfmperiod

For local development:

pip install -e .[dev]

Quick Start

import numpy as np
from acfmperiod import PerACF, MPerACF, CovCorPer

rng = np.random.default_rng(7)
x = rng.normal(size=(128, 2))

res = PerACF(x, lagmax=20, typevalue="correlation", doplot=False)
print(res.acf.shape)   # (20, 2, 2)

rob = MPerACF(x, lagmax=20, doplot=False)
cov0 = CovCorPer(x, typevalue="covariance")
print(cov0)

API

  • PerioReg, MPerioReg
  • CrossPeriodogram, MCrossPeriodogram
  • PerACF, MPerACF
  • CovCorPer, CovCorMPer
  • plotrobacf

Lowercase aliases are available without underscore naming. ß

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