Proximity fuzzy clustering and its application to time series clustering and prediction

Abstract

A new time series prediction architecture is introduced using a fuzzy inference system (FIS) and a new framework for fuzzy relational clustering of time series. The FIS is used to predict future samples in a time series where recurrent neural networks comprise the consequents of the rules. The antecedents come in the form of fuzzy relations; however, previous approaches such as FCM build these antecedents in a Euclidean feature space which is very limiting and not well suited to the problem of clustering time series. Our approach to learning the antecedents of the rules involves clustering time series using proximity values, indicative of closeness. A variant of the classical correlation is used to measure proximity. Our objective is to investigate and evaluate the application of proximity fuzzy clustering in the domain of time series prediction by comparing its performance against several commonly used time series prediction models.