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

TL;DR

This paper introduces an R package that implements the Pattern Sequence based Forecasting (PSF) algorithm for univariate time series prediction, combining clustering and pattern-based prediction to improve forecasting accuracy.

Contribution

The paper presents a comprehensive R package for the PSF algorithm, including functions for clustering, pattern detection, and automated optimization, facilitating easier implementation and promotion of the method.

Findings

PSF package effectively automates forecasting tasks.

Compared to auto.arima and ets, PSF shows competitive or improved accuracy.

The package simplifies applying the PSF algorithm to various fields.

Abstract

This paper discusses about an 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 implementation with minimum efforts. This paper describes all the functions in the PSF package with their syntax. It also provides a simple example of usage. 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.