This paper introduces the R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF algorithm. It also contains a function which automates all other functions to obtain optimized prediction results. The aim of this package is to promote the PSF algorithm and to ease its usage with minimum efforts. This paper describes all the functions in the PSF package with their syntax. It also provides a simple example. Finally, the usefulness of this package is discussed by comparing it to auto.arima and ets, well-known time series forecasting functions available on CRAN repository.
PSF, cluster, data.table, forecast
Environmetrics, Finance, Cluster, Econometrics, HighPerformanceComputing, Multivariate, TimeSeries
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Bokde, et al., "The R Journal: PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm", The R Journal, 2017
BibTeX citation
@article{RJ-2017-021,
author = {Bokde, Neeraj and Asencio-Cortés, Gualberto and Martínez-Álvarez, Francisco and Kulat, Kishore},
title = {The R Journal: PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-021},
doi = {10.32614/RJ-2017-021},
volume = {9},
issue = {1},
issn = {2073-4859},
pages = {324-333}
}