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: <>) and by Bob Carpenter (2012, URL: <>).

Version: 2.0.2
Imports: bspec (≥ 1.5), dplyr (≥, fftwtools (≥ 0.9.8), pracma (≥ 2.1.4), zoo (≥ 1.8-3)
Published: 2020-09-29
Author: Maximilian Toller [aut], Tiago Santos [aut, cre], Roman Kern [aut]
Maintainer: Tiago Santos <teixeiradossantos at>
License: GPL-2
NeedsCompilation: no
Citation: sazedR citation info
Materials: README NEWS
In views: TimeSeries
CRAN checks: sazedR results


Reference manual: sazedR.pdf
Package source: sazedR_2.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: sazedR_2.0.2.tgz, r-oldrel: sazedR_2.0.2.tgz
Old sources: sazedR archive


Please use the canonical form to link to this page.