.png){kind=icon}
返回
Multivariate time series forecasting via attention-based encoder-decoder framework
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2019.12.118.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Time series forecasting is an important technique to study the behavior of temporal data and forecast future values, which is widely applied in many fields, e.g. air quality forecasting, power load forecasting, medical monitoring, and intrusion detection. In this paper, we firstly propose a novel temporal attention encoder-decoder model to deal with the multivariate time series forecasting problem. It is an end-to-end deep learning structure that integrates the traditional encode context vector and temporal attention vector for jointly temporal representation learning, which is based on bi-directional long short-term memory networks (Bi-LSTM) layers with temporal attention mechanism as the encoder network to adaptively learning long-term dependency and hidden correlation features of multivariate temporal data. Extensive experimental results on five typical multivariate time series datasets showed that our model has the best forecasting performance compared with baseline methods. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Multivariate time series
Temporal attention
Multi-step forecasting
Encoder-decoder
Deep learning
Long short-term memory networks
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Multi-step-ahead time series prediction using multiple-output support vector regression.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏Univariate versus multivariate time series forecasting: an application to international tourism demand单变量与多变量时间序列预测: 在国际旅游需求中的应用
PREAI
分享收藏Time series forecasting using a deep belief network with restricted Boltzmann machines使用具有受限玻尔兹曼机的深度信念网络进行时间序列预测NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容