A new algorithm for time series prediction by temporal fuzzy clustering

We present a new algorithm for time series prediction using temporal fuzzy clustering. The algorithm is based on the framework of temporal clustering that was applied successfully to analyze, segment and recognize patterns of non-stationary signals in applications such as speech recognition and biomedical signal analysis. We combine fuzzy clustering in the observation space and cluster validation in the time axis in order to generate a prediction according to the online estimation of a time varying multivariate mixture distribution function that underlies the series elements. The resulting temporal behavior of the membership matrices can also be used to extract a prediction on the future probability distribution function (PDF) of the time series. The algorithm is more feasible than common methods such as hidden markov models (HMM) in predicting non-stationary signals with a slow drift in their PDF and is also more efficient from a computation standpoint.