多元统计
计算机科学
前馈
感知器
人工智能
时间序列
多层感知器
特征(语言学)
系列(地层学)
前馈神经网络
过程(计算)
机器学习
模式识别(心理学)
数据挖掘
人工神经网络
操作系统
工程类
控制工程
哲学
古生物学
生物
语言学
作者
Yuntong Liu,Chunna Zhao,Yaqun Huang
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 88644-88654
被引量:3
标识
DOI:10.1109/access.2022.3192430
摘要
Multivariate time series forecasting has very great practical significance for a long time, and it has been attracting the attention of researchers from a diverse range of fields. However, it is difficult to analyze the relationship and transformation law among multivariate data. Further, it is hard to obtain a relatively accurate prediction. In recent years, long short-term memory (LSTM) has shown high capability in dealing with nonlinearity and long memory of time series data. Although LSTM can also process multivariate data, it is insufficient to pay various degrees of attention to multivariate data. To address this issue, a multivariate time series prediction model based on multilayer perceptron (MLP), feedforward attention mechanism, and LSTM is proposed in this paper. Firstly, the simulation process utilizes the MLP module to map the multivariate initial sequences into another latent dimensional space, thereby obtaining easily captured mapping features. Then, these features are adaptively assigned attention weights through the feed-forward attention mechanism. Finally, the LSTM module uses these feature sequences with attention weights to make final predictions. The experimental results show that the method that combines the MLP layer with the feed-forward attention layer is effective in extracting multivariate features. Also, the empirical results indicate that our proposed framework (a combined model of MLP-Feedforward attention-LSTM) can achieve better performance than baselines.
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