The R Journal: article published in 2017, volume 9:1

PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm
Neeraj Bokde, Gualberto Asencio-Cortés, Francisco Martínez-Álvarez and Kishore Kulat , The R Journal (2017) 9:1, pages 324-333.

Abstract 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.

Received: 2016-09-12; online 2017-05-10
CRAN packages: PSF, cluster, data.table, forecast
CRAN Task Views implied by cited CRAN packages: Environmetrics, Finance, Cluster, Econometrics, HighPerformanceComputing, Multivariate, TimeSeries

This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-021,
  author = {Neeraj Bokde and Gualberto Asencio-Cortés and Francisco
          Martínez-Álvarez and Kishore Kulat},
  title = {{PSF: Introduction to R Package for Pattern Sequence Based
          Forecasting Algorithm}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-021},
  url = {https://doi.org/10.32614/RJ-2017-021},
  pages = {324--333},
  volume = {9},
  number = {1}
}