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This paper discusses about an R package that implements the Pattern Sequencebased Forecasting (PSF) algorithm, which was developed for univariate timeseries forecasting. This algorithm has been successfully applied to manydifferent fields. The PSF algorithm consists of two major parts: clustering andprediction. The clustering part includes selection of the optimum number ofclusters. It labels time series data with reference to such clusters. Theprediction part includes functions like optimum window size selection forspecific patterns and prediction of future values with reference to pastpattern sequences. The PSF package consists of various functions to implementthe PSF algorithm. It also contains a function which automates all otherfunctions to obtain optimized prediction results. The aim of this package is topromote the PSF algorithm and to ease its implementation with minimum efforts.This paper describes all the functions in the PSF package with their syntax. Italso provides a simple example of usage. Finally, the usefulness of thispackage is discussed by comparing it to auto.arima and ets, well-known timeseries forecasting functions available on CRAN repository.

PSF : Introduction to R Package for Pattern Sequence Based Forecasting Algorithm