An improved quantile-point-based evolutionary segmentation representation method of financial time series

Abstract

Abstract: Effective and concise feature representation is crucial for time series mining. However, traditional time series feature representation approaches are inadequate for Financial Time Series (FTS) due to FTS' complex, highly noisy, dynamic and non-linear characteristics. Thus, we proposed an improved linear segmentation method named MS-BU-GA in this work. The critical data points that can represent financial time series are added to the feature representation result. Specifically, firstly, we propose a division criterion based on the quantile segmentation points. On the basis of this criterion, we perform segmentation of the time series under the constraint of the maximum segment fitting error. Then, a bottom-up mechanism is adopted to merge the above segmentation results under the maximum segment fitting error. Next, we apply Genetic Algorithm (GA) to the merged results for further optimization, which reduced the overall segment representation fitting error and the integrated factor of segment representation error and number of segments. The experimental result shows that the MS-BU-GA has outperformed existing methods in segment number and representation error. The overall average representation error is decreased by 21.73% and the integrated factor of the number of segments and the segment representation error is reduced by 23.14%.