Authors
Yoshihide Koyama, Tetsuo Hattori, Hiromichi Kawano
Corresponding Author
Yoshihide Koyama
Available Online 30 June 2014.
DOI
https://doi.org/10.2991/jrnal.2014.1.1.11
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 ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).