GTM through time

The standard GTM (generative topographic mapping) algorithm assumes that the data on which it is trained consists of independent, identically distributed (i.i.d.) vectors. For time series, however, the i.i.d. assumption is a poor approximation. In this paper we show how the GTM algorithm can be exte...

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Hauptverfasser:	Bishop, C.M, Hinton, G.E, Strachan, I.G.D
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Adaptive system theory Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Computer science control theory systems Connectionism. Neural networks Exact sciences and technology Inference from stochastic processes time series analysis Mathematics Neural computing techniques Neural nets Other topics in statistics Probability and statistics Sciences and techniques of general use Statistics Theoretical computing
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Zusammenfassung:	The standard GTM (generative topographic mapping) algorithm assumes that the data on which it is trained consists of independent, identically distributed (i.i.d.) vectors. For time series, however, the i.i.d. 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.
ISSN:	0537-9989
DOI:	10.1049/cp:19970711