New scheme for extracting multi-temporal sequence patterns

Previous research has been dedicated to clustering and predicting time series. Practically, we may hope to extract all recurring temporal patterns out of a temporal signal sequence. This paper proposes a new scheme for unsupervised multi-temporal sequence pattern extraction. The main idea of the scheme is iterative coarse to fine data examination. We decompose a pattern into ambiguous sub-patterns (ASP) and distinguishable sub-patterns (DSP). In each iteration, we coarsely examine the training temporal signal sequence by training an Elman neural network. The trained Elman network is used to select the DSP candidate set. Then, we look at the training signals around the DSPs and use maximum likelihood criteria to expand them into whole patterns. We cut out the newfound patterns from the training signal sequence and repeat the whole procedure until no more new patterns are found. The experimental result shows this method is promising.