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Extended SPRT for structural change detection of time series based on a multiple regression model

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Abstract

It is important to detect a structural change in a time series quickly as a trigger to remodeling the forecasting model. The well-known Chow test has been used as the standard method for detecting change, especially in economics. However, we have proposed the application of the sequential probability ratio test (SPRT) for detecting the change in single-regression modeled time-series data. In this article, we show experimental results using SPRT and the Chow test when applied to time-series data that are based on multiple regression models. We also clarify the effectiveness of SPRT compared with the Chow test in its ability to detect change early and correctly, and its computational complexity. Moreover, we extend the definition of the point at which structural change is detected with the SPRT method, and show an improvement in the accuracy of change detection.

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References

  1. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  2. Brockwell PJ, Davis RA (2003) Introduction to time series and forecasting. 2nd edn. Springer

  3. Han C, Willet PK, Abraham DA (1999) Some methods to evaluate the performance of Page’s test as used to detect transient signals. IEEE Trans Signal Process 47(8):2112–2127

    Article  Google Scholar 

  4. Blostein SD (1991) Quickest detection of a time-varying change in distribution. IEEE Trans Inf Theor 37(4):1116–1122

    Article  Google Scholar 

  5. Wald A (1947) Sequential analysis. Wiley, New York

    MATH  Google Scholar 

  6. Kawano H, Hattori T, Nishimatsu K (2008) Structural change point detection method of time series using sequential probability ratio test: comparison with Chow test in the ability of early detection (in Japanese). IEEJ Trans EIS 128(4):583–592

    Article  Google Scholar 

  7. Chow GC (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28(3):591–605

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Graduate School of Engineering, Kagawa University, 2217-20 Hayashi, Takamatsu, Kagawa, 761-0396, Japan

    Katsunori Takeda & Tetsuo Hattori

  2. Micro Technica Co., Tokyo, Japan

    Tetsuya Izumi

  3. NTT Advanced Technology, Tokyo, Japan

    Hiromichi Kawano

Authors
  1. Katsunori Takeda
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  2. Tetsuo Hattori
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  3. Tetsuya Izumi
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  4. Hiromichi Kawano
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Corresponding author

Correspondence to Tetsuo Hattori.

Additional information

This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010

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Takeda, K., Hattori, T., Izumi, T. et al. Extended SPRT for structural change detection of time series based on a multiple regression model. Artif Life Robotics 15, 417–420 (2010). https://doi.org/10.1007/s10015-010-0833-4

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  • DOI: https://doi.org/10.1007/s10015-010-0833-4

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