系列(地层学)
核(代数)
计算机科学
特征(语言学)
人工智能
一般化
时间序列
水准点(测量)
支持向量机
核方法
机器学习
模式识别(心理学)
模糊认知图
数据挖掘
模糊逻辑
数学
模糊集
模糊分类
地理
数学分析
哲学
组合数学
生物
古生物学
语言学
大地测量学
作者
Kaixin Yuan,Jing Liu,Shanchao Yang,Kai Wu,Fang Shen
标识
DOI:10.1016/j.knosys.2020.106359
摘要
Fuzzy cognitive maps (FCMs) have emerged as a powerful tool for dealing with the task of time series prediction. Most existing research devoted to designing an effective method to extract feature time series from the original time series, which are used to construct FCMs and predict the time series. However, in existing methods, all extracted feature time series, including the redundant feature time series, were used to develop FCMs instead of selecting the key feature time series (KFTS) to construct FCMs, which limits the generalization and prediction accuracy of the models. In this paper, we propose a framework based on kernel mapping and high-order FCMs (HFCM) to forecast time series inspired by the kernel methods and support vector regression (SVR). The model is termed as Kernel-HFCM. Kernel mapping is designed to map the original one-dimensional time series into multidimensional feature time series, and then the feature selection algorithm is proposed to select the KFTS from the multidimensional feature time series to develop the HFCM. Finally, reverse kernel mapping is used to map the feature time series back to the predicted one-dimensional time series. In comparison to the existing methods, the experimental results on seven benchmark datasets demonstrate the effectiveness of Kernel-HFCM in time series prediction.
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