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

  • Authors:
  • Katsunori Takeda;Tetsuo Hattori;Tetsuya Izumi;Hiromichi Kawano

  • Affiliations:
  • Graduate School of Engineering, Kagawa University, Kagawa, Japan 761-0396;Graduate School of Engineering, Kagawa University, Kagawa, Japan 761-0396;Micro Technica Co., Tokyo, Japan;NTT Advanced Technology, Tokyo, Japan

  • Venue:
  • Artificial Life and Robotics
  • Year:
  • 2010

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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.