Abstract
The problem of time series can be classified into three types, in a practical sense. The first problem is how to generate a prediction model that adequately represents the characteristics of the early time series data. The second problem is how to quickly detect the structural change of the time series, where the estimated prediction model does not meet the real data any longer. The third problem is how to correct the time series model after the change detection. This paper focuses on the second problem and proposes a novel method for quick detection of the structural change point in time series. The proposed method is based on a sequential probability ratio test that has been mainly used in the field of quality control. This paper discusses the features of the method from numerical experimentation results. And also, this paper shows its effectiveness, in comparison with the well-known Chow Test.
