Hierarchical-fuzzy clustering of temporal-patterns and its application for time-series prediction

In a recent paper we presented a new algorithm for hierarchical unsupervised fuzzy clustering (HUFC) and demonstrated its performance for biomedical state identification. In the present paper, a new hybrid algorithm for time series prediction is applying the HUFC algorithm for grouping and modeling related temporal-patterns that are dispersed along a non-stationary signal. Vague and gradual changes in regime are naturally treated by means of fuzzy clustering. An adaptive hierarchical selection of the number of clusters (the number of underlying processes) can overcome the general non-stationary nature of real-life time-series (biomedical, physical, economical, etc.).