This paper consides the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they outperform existing methods.
|Title of host publication||Proceedings of the 1998 IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing VIII, 1998|
|Editors||Tony Constantinides, S. Y. Kung, Mahesan Niranjan, Elizabeth Wilson|
|Place of Publication||United States|
|Publisher||IEEE Computer Society|
|Number of pages||7|
|Publication status||Published - 1 Sep 1998|
|Name||Proceedings of the 1998 IEEE Signal Processing Society Workshop|
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- non-linear, non-stationary environment, Hidden Markov Models, synthetic data, real data, oil drilling process