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.
Highlights
Pattern Sequence based Forecasting (PSF) stands for Pattern Sequence Forecasting algorithm
Martínez-Álvarez et al (2011a) improved the label based forecasting (LBF) algorithm proposed in Martínez-Álvarez et al (2008) to forecast the electricity price and compared it to other available forecasting algorithms such as ANN (Catalão et al, 2007), ARIMA (Conejo et al, 2005), mixed models (García-Martos et al, 2007) and WNN (Troncoso et al, 2007)
As output of the clustering process, the original time series data is converted into a series of labels, which is used as input in the prediction block of the second phase of the PSF algorithm
Summary
PSF stands for Pattern Sequence Forecasting algorithm. PSF is a successful forecasting technique based on the assumption that there exist pattern sequences in the target time series data. As output of the clustering process, the original time series data is converted into a series of labels, which is used as input in the prediction block of the second phase of the PSF algorithm. In the PSF package, the optimum window size selection is done with the function optimum_w(), which takes as input the time series data, the previous estimated k value, a set candidate w values to search in and the cycle of the input time series.
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