首页|A Robust Fuzzy Time Series Forecasting Method Based on Multi-partition and Outlier Detection
A Robust Fuzzy Time Series Forecasting Method Based on Multi-partition and Outlier Detection
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NETL
NSTL
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维普
We propose a robust fuzzy time series forecasting method based on multi-partition approach and outlier detection for forecasting market prices.The multi-partition 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.
Fuzzy time seriesMulti-partition approachOutlier detection
QU Hua、ZHANG Yanpeng、LIU Wei、ZHAO Jihong
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School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China
School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Suzhou Caiyun Networking Technologies Company Limited, Suzhou 215000, China
School of Communication and Information Engineering, Xi'an University of Posts and Telecommunication,Xi'an 710061 , China
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This work is supported by the State Key Program of National Natural Science Foundation of ChinaNational Major Project
NETL
NSTL
万方数据
维普
