Duration and Interval Hidden Markov Model for Sequential Data Analysis
This addresses the need for better sequential data analysis tools in data modeling, though it appears incremental by extending HMMs with duration and interval features.
The paper tackled the problem of modeling complex sequential event data by proposing the Duration and Interval Hidden Markov Model (DI-HMM) to represent state duration and interval, resulting in efficient and flexible sequential data retrieval as demonstrated in numerical experiments.
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.