.png){kind=icon}
返回
Spatial-Temporal Convolutional Transformer Network for Multivariate Time Series Forecasting
.png){kind=icon}
OA
AI
DOI：10.3390/s22030841.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods suffer from two major shortcomings: (1) They ignore the local context semantics when modeling temporal dependencies. (2) They lack the ability to capture the spatial dependencies of multiple patterns. To tackle such issues, we propose a novel Transformer-based model for multivariate time series forecasting, called the spatial-temporal convolutional Transformer network (STCTN). STCTN mainly consists of two novel attention mechanisms to respectively model temporal and spatial dependencies. Local-range convolutional attention mechanism is proposed in STCTN to simultaneously focus on both global and local context temporal dependencies at the sequence level, which addresses the first shortcoming. Group-range convolutional attention mechanism is designed to model multiple spatial dependency patterns at graph level, as well as reduce the computation and memory complexity, which addresses the second shortcoming. Continuous positional encoding is proposed to link the historical observations and predicted future values in positional encoding, which also improves the forecasting performance. Extensive experiments on six real-world datasets show that the proposed STCTN outperforms the start-of-the-art methods and is more robust to nonsmooth time series data.
Keyword:
multivariate time series forecasting
spatiotemporal
convolutional Transformer
attention mechanism
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:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy.png)
EntropyOAAI
EntropyIF0
OAAI分享收藏Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation NetworksSENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks基于递归注意机制和生成对抗网络的时间序列预测和分类模型SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容