sazedR: Parameter-Free Domain-Agnostic Season Length Detection in Time Series

Spectral and Average Autocorrelation Zero Distance Density ('sazed') is a method for estimating the season length of a seasonal time series. 'sazed' is aimed at practitioners, as it employs only domain-agnostic preprocessing and does not depend on parameter tuning or empirical constants. The computation of 'sazed' relies on the efficient autocorrelation computation methods suggested by Thibauld Nion (2012, URL: <http://www.tibonihoo.net/literate_musing/autocorrelations.html>) and by Bob Carpenter (2012, URL: <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).

Version: 1.0.0
Imports: bspec (≥ 1.5), fftwtools (≥ 0.9.8), forecast (≥ 8.4), pracma (≥ 2.1.4), signal (≥ 0.7.6), zoo (≥ 1.8-3)
Published: 2018-09-30
Author: Maximilian Toller [aut], Tiago Santos [aut, cre], Roman Kern [aut]
Maintainer: Tiago Santos <teixeiradossantos at tugraz.at>
License: GPL-2
NeedsCompilation: no
Materials: README
CRAN checks: sazedR results

Downloads:

Reference manual: sazedR.pdf
Package source: sazedR_1.0.0.tar.gz
Windows binaries: r-devel: sazedR_1.0.0.zip, r-release: sazedR_1.0.0.zip, r-oldrel: sazedR_1.0.0.zip
OS X binaries: r-release: sazedR_1.0.0.tgz, r-oldrel: sazedR_1.0.0.tgz

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