Hidden markov model-based time series prediction using motifs for detecting inter-time-serial correlations

This paper presents an approach for time series prediction using a Hidden Markov Model, which bases on inter-time-serial correlations. These correlations between time series of a given database are automatically discovered by hierarchically clustering motif-based time series representations, which can be used for the prediction of the future development of one time series on base of known values from the one and correlated time series. The functionality and the influence of the different parameters of the motif-based representation, the inter-time-serial correlation discovery and the prediction capability are evaluated on two large databases of river level measurements and stock data.

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