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.
|Suggests:||knitr, rmarkdown, forecast|
|Author:||Neeraj Bokde, Gualberto Asencio-Cortes and Francisco Martinez-Alvarez|
|Maintainer:||Neeraj Bokde <neerajdhanraj at gmail.com>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|Citation:||PSF citation info|
|CRAN checks:||PSF results|
Introduction to Pattern Sequence based Forecasting (PSF) algorithm
|Windows binaries:||r-devel: PSF_0.5.zip, r-release: PSF_0.5.zip, r-oldrel: PSF_0.5.zip|
|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 imports:||decomposedPSF, ForecastTB|
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