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MPM: Multi Patterns Memory Model for Short-Term Time Series Forecasting
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DOI：10.1109/TKDE.2024.3490843.png)
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摘要
En 中文
Short-term time series forecasting is pivotal in various scientific and industrial fields. Recent advancements in deep learning-based technologies have significantly improved the efficiency and accuracy of short-term time series modeling. Despite advancements, current time short-term series forecasting methods typically emphasize modeling dependencies across time stamps but frequently overlook inter-variable dependencies, which is crucial for multivariate forecasting. We propose a multi patterns memory model discovering various dependency patterns for short-term multivariate time series forecasting to fill the gap. The proposed model is structured around two key components: the short-term memory block and the long-term memory block. These networks are distinctively characterized by their use of asymmetric convolution, each tailored to process the various spatial-temporal dependencies among data. Experimental results show that the proposed model demonstrates competitive performance over the other time series forecasting methods across five benchmark datasets, likely thanks to the asymmetric structure, which can effectively extract the underlying various spatial-temporal dependencies among data.
Keyword:
Time series analysis
Forecasting
Predictive models
Transformers
Long short term memory
Convolution
Computational modeling
Feature extraction
Data models
Attention mechanisms
Asymmetric convolution
multi patterns memory model
spatial-temporal dependency
time series forecasting
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
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