The standard GTM (generative topographic mapping) algorithm assumes that the data on which it is trained consists of independent, identically distributed (iid) vectors. For time series, however, the iid assumption is a poor approximation. In this paper we show how the GTM algorithm can be extended to model time series by incorporating it as the emission density in a hidden Markov model. Since GTM has discrete hidden states we are able to find a tractable EM algorithm, based on the forward-backward algorithm, to train the model. We illustrate the performance of GTM through time using flight recorder data from a helicopter.
|Title of host publication||Fifth International Conference on Artificial Neural Networks|
|Place of Publication||Cambridge, US|
|Number of pages||6|
|Publication status||Published - 9 Jul 1997|
|Event||Proceedings IEE Fifth International Conference on Artificial Neural Networks - |
Duration: 9 Jul 1997 → 9 Jul 1997
|Conference||Proceedings IEE Fifth International Conference on Artificial Neural Networks|
|Period||9/07/97 → 9/07/97|
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- Generative topographic mapping
- identically distributed vectors
- time series
- Markov model