Modelling switching dynamics using prediction experts operating on distinct wavelet scales
Résumé
We present a framework for modelling the switching dynamics of a time series with correlation structures spanning distinct time scales, based on a neural-based multi-expert prediction model. First, an orthogonal wavelet transform is used to decompose the time series into varying levels of temporal resolution so that the underlying temporal structures of the original time series become more tractable. The transitions between the resolution scales are assumed to be governed by a hidden Markov model (HMM). The best state sequence is obtained by the Viterbi algorithm assuming some prior knowledge on the state transition probabilities and energy-dependent observation probabilities. The model achieves a hard segmentation of the time series into distinct dynamical modes and the simultaneous specialization of the prediction experts on the segments. The predictive ability of this strategy is assessed on a synthetic time series.
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