Modelling switching dynamics using prediction experts operating on distinct wavelet scales - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2006

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.
Fichier principal
Vignette du fichier
WAVELET_ESANN2006.pdf (185.53 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00266051 , version 1 (20-03-2008)

Identifiants

  • HAL Id : hal-00266051 , version 1

Citer

Alexandre Aussem, Pierre Chainais. Modelling switching dynamics using prediction experts operating on distinct wavelet scales. European Symposium on Artificial Neural Networks, ESANN'06, 2006, Bruges, Belgium. pp. ?. ⟨hal-00266051⟩
213 Consultations
47 Téléchargements

Partager

Gmail Facebook X LinkedIn More