This paper considers 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||Neural Networks for Signal Processing - Proceedings of the IEEE Workshop|
|Editors||M. Niranjan, E. Wilson, T. Constantinides, S.Y. Kung|
|Place of Publication||United States|
|Publisher||IEEE Computer Society|
|Number of pages||7|
|Publication status||Published - 1 Jan 1998|
Azzouzi, M., & Nabney, I. T. (1998). Analyzing time series structure with Hidden Markov Models. In M. Niranjan, E. Wilson, T. Constantinides, & S. Y. Kung (Eds.), Neural Networks for Signal Processing - Proceedings of the IEEE Workshop (pp. 402-408). IEEE Computer Society.