Segmenting time series with a hybrid neural networks - Hidden Markov model

Laura Firoiu, Paul R. Cohen

Research output: Contribution to conferencePaper

11 Scopus citations

Abstract

This paper describes work on a hybrid HMM/ANN system for finding patterns in a time series, where a pattern is a function that can be approximated by a recurrent neural network embedded in the state of a hidden Markov model. The most likely path of the hidden Markov model is used both for re-training the HMM/ANN model and for segmenting the time series into pattern occurrences. The number of patterns is determined from the data by first increasing the number of networks as long as the likelihood of the segmentation increases, then reducing this number to satisfy an MDL criterion. In experiments with artificial data the algorithm correctly identified the generating functions. Preliminary results with robot data show that potentially useful patterns that can be associated with low-level concepts can be induced this way.

Original languageEnglish (US)
Pages247-252
Number of pages6
StatePublished - Dec 1 2002
Event18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02) - Edmonton, Alta., Canada
Duration: Jul 28 2002Aug 1 2002

Other

Other18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02)
CountryCanada
CityEdmonton, Alta.
Period7/28/028/1/02

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Firoiu, L., & Cohen, P. R. (2002). Segmenting time series with a hybrid neural networks - Hidden Markov model. 247-252. Paper presented at 18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02), Edmonton, Alta., Canada.