A new algorithm for time series prediction by temporal fuzzy clustering

• Published in: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 Vol. 2; pp. 728 - 731 vol.2
• Main Authors: Policker, S., Geva, A.B.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2000
• Subjects: Algorithm design and analysis; Clustering algorithms; Distribution functions; Hidden Markov models; Pattern analysis; Pattern recognition; Signal analysis; Speech analysis; Speech recognition; Time varying systems
• ISBN: 0769507506; 9780769507507
• ISSN: 1051-4651; 2831-7475
• DOI: 10.1109/ICPR.2000.906178

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 nonstationary 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 nonstationary signals with a slow drift in their PDF and is also more efficient from a computation standpoint.