Duration and Interval Hidden Markov Model for Sequential Data Analysis

TL;DR

The paper introduces DI-HMM, a new model that effectively captures state durations and intervals in sequential data, enhancing data representation and retrieval accuracy.

Contribution

It proposes the Duration and Interval Hidden Markov Model (DI-HMM), a novel approach for modeling complex time-series data with explicit duration and interval representations.

Findings

DI-HMM outperforms existing models in accuracy.

Demonstrated efficiency on synthetic and real datasets.

Provides flexible and practical sequential data analysis.

Abstract

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.