Model Introduced SPRT for Structural Change Detection of Time Series (I) -- Formulation --

DOI

10.2991/jrnal.2014.1.1.11How to use a DOI?

Keywords

Time series, Change detection, SPRT (Sequential Probability Ratio Test), Hidden Markov Model

Abstract

Previously, we have proposed a method applying Sequential Probability Ratio Test (SPRT) to the structural change detection problem of ongoing time series data. In this paper, we introduce a structural change model with Poisson process into a system that outputs a set of ongoing time series data, moment by moment. The model can be considered as a kind of Hidden Markov Model. According to the model, we formulate a method to find out the structural change, by defining a New Sequential Probability Ratio (NSPR), which can be calculated from the joint occurrence probability of the observing event with the event H0 (the structural change is not occurred) and H1 (the change is occurred). And also, we show the simple recurrence equation of the NSPR.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yoshihide Koyama
AU  - Tetsuo Hattori
AU  - Hiromichi Kawano
PY  - 2014
DA  - 2014/06/30
TI  - Model Introduced SPRT for Structural Change Detection of Time Series (I) -- Formulation --
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 54
EP  - 59
VL  - 1
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2014.1.1.11
DO  - 10.2991/jrnal.2014.1.1.11
ID  - Koyama2014
ER  -