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Duration and Interval Hidden Markov Model for Sequential Data Analysis

08/20/2015

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by Hiromi Narimatsu, et al.

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University of Electro-Communications

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Analysis of sequential event data has been recognized as one of the essential tools in data modeling and analysis field. In this paper, after the examination of its technical requirements and issues to model complex but practical situation, we propose a new sequential data model, dubbed Duration and Interval Hidden Markov Model (DI-HMM), that efficiently represents "state duration" and "state interval" of data events. This has significant implications to play an important role in representing practical time-series sequential data. This eventually provides an efficient and flexible sequential data retrieval. Numerical experiments on synthetic and real data demonstrate the efficiency and accuracy of the proposed DI-HMM.

Hiromi Narimatsu
2 publications
Hiroyuki Kasai
23 publications

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