Model Introduced SPRT for Structural Change Detection of Time Series (I) -- Formulation --
- 10.2991/jrnal.2014.1.1.11How to use a DOI?
- Time series, Change detection, SPRT (Sequential Probability Ratio Test), Hidden Markov Model
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.
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- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
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 -