State duration and interval modeling in hidden semi-Markov model for sequential data analysis

Hiromi Narimatsu, Hiroyuki Kasai

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Sequential data modeling and analysis have become indispensable tools for analyzing sequential data, such as time-series data, because larger amounts of sensed event data have become available. These methods capture the sequential structure of data of interest, such as input-output relations and correlation among datasets. However, because most studies in this area are specialized or limited to their respective applications, rigorous requirement analysis of such models has not been undertaken from a general perspective. Therefore, we particularly examine the structure of sequential data, and extract the necessity of “state duration” and “state interval” of events for efficient and rich representation of sequential data. Specifically addressing the hidden semi-Markov model (HSMM) that represents such state duration inside a model, we attempt to add representational capability of a state interval of events onto HSMM. To this end, we propose two extended models: an interval state hidden semi-Markov model (IS-HSMM) to express the length of a state interval with a special state node designated as “interval state node”; and an interval length probability hidden semi-Markov model (ILP-HSMM) which represents the length of the state interval with a new probabilistic parameter “interval length probability.” Exhaustive simulations have revealed superior performance of the proposed models in comparison with HSMM. These proposed models are the first reported extensions of HMM to support state interval representation as well as state duration representation.

Original languageEnglish
Pages (from-to)377-403
Number of pages27
JournalAnnals of Mathematics and Artificial Intelligence
Volume81
Issue number3-4
DOIs
Publication statusPublished - 2017 Dec 1
Externally publishedYes

Keywords

  • Duration
  • HSMM
  • Interval
  • Sequential data analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

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