PSF: Forecasting of Univariate Time Series Using the Pattern Sequence-Based Forecasting (PSF) Algorithm

Pattern Sequence Based Forecasting (PSF) takes univariate time series data as input and assist to forecast its future values. This algorithm forecasts the behavior of time series based on similarity of pattern sequences. Initially, clustering is done with the labeling of samples from database. The labels associated with samples are then used for forecasting the future behaviour of time series data. The further technical details and references regarding PSF are discussed in Vignette.

Version: 0.5
Imports: data.table, cluster
Suggests: knitr, rmarkdown, forecast
Published: 2022-05-01
DOI: 10.32614/CRAN.package.PSF
Author: Neeraj Bokde, Gualberto Asencio-Cortes and Francisco Martinez-Alvarez
Maintainer: Neeraj Bokde <neerajdhanraj at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: PSF citation info
In views: TimeSeries
CRAN checks: PSF results


Reference manual: PSF.pdf
Vignettes: Introduction to Pattern Sequence based Forecasting (PSF) algorithm


Package source: PSF_0.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): PSF_0.5.tgz, r-oldrel (arm64): PSF_0.5.tgz, r-release (x86_64): PSF_0.5.tgz, r-oldrel (x86_64): PSF_0.5.tgz
Old sources: PSF archive

Reverse dependencies:

Reverse imports: decomposedPSF, ForecastTB


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