QU Hua, ZHANG Yanpeng, LIU Wei, ZHAO Jihong. A Robust Fuzzy Time Series Forecasting Method Based on Multi-partition and Outlier Detection[J]. Chinese Journal of Electronics, 2019, 28(5): 899-905. doi: 10.1049/cje.2019.06.001
Citation: QU Hua, ZHANG Yanpeng, LIU Wei, ZHAO Jihong. A Robust Fuzzy Time Series Forecasting Method Based on Multi-partition and Outlier Detection[J]. Chinese Journal of Electronics, 2019, 28(5): 899-905. doi: 10.1049/cje.2019.06.001

A Robust Fuzzy Time Series Forecasting Method Based on Multi-partition and Outlier Detection

doi: 10.1049/cje.2019.06.001
Funds:  This work is supported by the State Key Program of National Natural Science Foundation of China (No.61531013) and the National Major Project (No.2018ZX030001016).
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  • Author Bio:

    QU Hua is a professor of Xi'an Jiaotong University,China.He received his B.A.degree from Nanjing University of Posts and Telecommunications,China,and his Ph.D.degree from Xi'an Jiaotong University.His research interests include mobile Internet,IP based network,network management and control,radio resource management in LTE-A system etc.He is a senior member of China Institute of Communications and also an editor of China Communications magazine.(Email:qh@xjtu.edu.cn)

  • Corresponding author: ZHANG Yanpeng (corresponding author) is working towards his Ph.D.degree in School of Software Engineering at Xi'an Jiaotong University,China.His research interest is the effects of user behaviors on the wireless networks,which includes handover decision,context-aware mobile and wireless network,etc.(Email:hnypzhang@gmail.com)
  • Received Date: 2017-06-26
  • Rev Recd Date: 2018-05-08
  • Publish Date: 2019-09-10
  • We propose a robust fuzzy time series forecasting method based on multi-partition approach and outlier detection for forecasting market prices. The multipartition approach employs a specific partition criterion for each dimension of the time series. We use a Gaussian kernel version fuzzy C-means clustering to construct the fuzzy logic relationships and detect the outliers by calculating the grade of membership. We apply an additional model, which is trained on the set of outliers by Levenberg-Marquardt algorithm, for forecasting the outliers in testing set. The experiment results show that the proposed method improves the robustness and the average forecasting accuracy rate.
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