Unsupervised Change Point Detection and Trend Prediction for Financial Time-Series Using a New CUSUM-Based Approach

Abstract

The aim of this research is to propose a binary segmentation algorithm to detect the change points in financial time-series based on the Iterative Cumulative Sum of Squares (ICSS). The proposed algorithm, entitled KW-ICSS, utilizes the non-parametric Kruskal-Wallis test in cross-validation procedures. In this regard, KW-ICSS can quickly detect the change points in non-normally distributed time-series with a small number of observations after the change points than the state-of-the-art ICSS algorithm, entitled AIT-ICSS. For the simulated financial time-series whose true location of the change point is known, KW-ICSS detects the change points with the average true positive rate of 81% for the different number of change points, whereas AIT-ICSS only exhibits 72.57%. Also, KW-ICSS’s mean absolute deviation between the true and detected change points is less than that of AIT-ICSS for different significance levels. The experiment also finds that the significance level, the model parameter, should be set to less than 10%. For the real-world financial time-series whose true location of change points is unknown, KW-ICSS’s robust detection of change points is observed from fewer detected change points and longer intervals between them. Furthermore, KW-ICSS’s trend prediction for the short-term future performs with an average of 92.47% accuracy, whereas AIT-ICSS shows 90.69%. Therefore, we claim that KW-ICSS successfully improves AIT-ICSS.

Keywords

• Unsupervised learning
• change point detection
• iterative cumulative sum of squares
• Kruskal-Wallis