The International Arab Journal of Information Technology (IAJIT)


An Improved Quantile-Point-Based Evolutionary

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%.

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[24] Zhu Y., Wu D., and Li S., “A Piecewise Linear Representation Method of Time Series Based on Feature Points,” in Proceedings of the International Conference on Knowledge- Based and Intelligent Information And Engineering Systems, Vietri sul Mare, pp. 1066- 1072, 2007. Lei Liu received the B.S. degree in Information and Computing Science from Sichuan Agricultural University, Ya’an, Sichuan in 2019. He is currently pursuing the master's degree with Xihua University, Chengdu, China. His research interests include machine learning and financial time series analysis. Zheng Pei received the M.S. and Ph.D. degrees from Southwest Jiaotong University, Chengdu, China, in 1999 and 2002, respectively. He is currently a Professor with the School of Science, Xihua University, Chengdu. He has nearly 100 research articles published in academic journals or conference. His research interests include rough set theory, fuzzy set theory, logical reasoning, and linguistic information processing. Peng Chen IEEE member, CCF member, received B.E. degree in computer science and technology from University of Electronic Science and Technology of China, M.Sc. degree in computer software and theory from Peking University and Ph.D. in computer science and technology from Sichuan University. He is currently a full professor of School of Computer and Software Engineering, Xihua University. His research interests include machine learning, service computing and time series analysis. Zhisheng Gao is an professor at the Xihua University. He received his Ph.D. degree in computer science from Sichuan University in 2012. He is the author of more than 50 journal papers. His current research interests include machine learning, image processing, and computer vision. Zhihao Gan received the B.E. degree in Internet of Things Engineering from Xihua University, Chengdu, China in 2020. He is currently pursuing the master's degree with Xihua University, Chengdu, China. His research interests include time series analysis and cloud computing. Kang Feng received the B.E. degree in Communication engineering from Nanjing University of Posts and Telecommunications, Nanjing, China in 2019. He is currently pursuing the master's degree with Xihua University, Chengdu, China. His research interests include financial time series forecasting and decision- making.