PSF : Introduction to R Package for Pattern Sequence Based Forecasting Algorithm
This work provides a tool for researchers and practitioners in time series analysis to easily apply the PSF algorithm, but it is incremental as it packages an existing method.
The paper introduces an R package implementing the Pattern Sequence based Forecasting (PSF) algorithm for univariate time series forecasting, and demonstrates its usefulness by comparing it to auto.arima and ets functions, showing competitive performance.
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.