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.

Author
Neeraj Bokde, Gualberto Asencio-Cortes and Francisco Martinez-Alvarez
Date of publication
2016-08-19 21:05:24
Maintainer
Neeraj Bokde <neerajdhanraj@gmail.com>
License
GPL (>= 2)
Version
0.3
URLs

View on CRAN

Man pages

psf
Forecasting of univariate time series using the PSF algorithm
psf_plot
Plot actual and forecasted values of an univariate time...

Files in this package

PSF
PSF/inst
PSF/inst/doc
PSF/inst/doc/PSF_vignette.Rmd
PSF/inst/doc/PSF_vignette.html
PSF/inst/doc/PSF_vignette.R
PSF/NAMESPACE
PSF/R
PSF/R/psf.R
PSF/R/convert_datatype.R
PSF/R/optimum_k.R
PSF/R/psf_plot.R
PSF/R/psf_predict.R
PSF/R/optimum_w.R
PSF/vignettes
PSF/vignettes/PSF_vignette.Rmd
PSF/MD5
PSF/build
PSF/build/vignette.rds
PSF/DESCRIPTION
PSF/man
PSF/man/psf_plot.Rd
PSF/man/psf.Rd