期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 29, 期 8, 页码 2336-2348出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2020.2998513
关键词
Time series analysis; Electroencephalography; Forecasting; Prediction algorithms; Brain modeling; Predictive models; Proposals; 1D-convolutional neural network (1D-CNN); elastic net; electroencephalogram (EEG); fuzzy cognitive map (FCM); time series prediction
资金
- General Program of National Natural Science Foundation of China [61773300]
A fast prediction model called ElasticNet(HFCM) is proposed in this article for handling multivariate long nonstationary time series, based on the combination of elastic net and high order fuzzy cognitive map. Experimental results demonstrate that the model can accurately and efficiently predict and classify EEG data.
Fuzzy cognitive maps (FCMs) have been successfully applied to time series forecasting. However, it still remains challenging to handle multivariate long nonstationary time series, such as EEG data, which may change rapidly and have patterns of trend. To overcome this limitation, in this article, we propose a fast prediction model to deal with multivariate long nonstationary time series based on the combination of elastic net and high order fuzzy cognitive map (HFCM), which is termed as ElasticNet(HFCM). The designed FCM models each variable by one node and the high-order FCM helps to capture the patterns of trend. A case study on predicting human actions through the Electroencephalogram (EEG) data in the form of multichannel long nonstationary time series is investigated based on the proposed prediction model. Specifically, we first predict EEG signals based on the historical data, then a 1D-convolutionary neural network (1D-CNN) is developed to classify the predicted time series. The experimental results on the Grasp-and-Lift dataset show that the proposal can predict the EEG data with lower prediction error compared with the other regression methods. The area under the curve scores obtained on the Grasp-and-Lift dataset by 1D-CNN are higher than those obtained by state-of-the-art classification methods for EEG data in most cases. These results illustrate that the proposal can predict and classify multivariate long nonstationary time series with high accuracy and efficiency.
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