  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.
