MLGN: multi-scale local-global feature learning network for long-term series forecasting

Although Transformer-based methods have achieved remarkable performance in the field of long-term series forecasting, they can be computationally expensive and lack the ability to specifically model local features as CNNs. CNN-based methods, such as temporal convolutional network (TCN), utilize conv...

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Bibliographic Details
Published in:Machine learning: science and technology 2023-12, Vol.4 (4), p.45059
Main Authors: Jiang, Maowei, Wang, Kai, Sun, Yue, Chen, Wenbo, Xia, Bingjie, Li, Ruiqi
Format: Article
Language:English
Subjects:
deep learning
Feature extraction
Forecasting
local-global feature extraction
long-term sequence forecasting
Machine learning
Mathematical models
multi-sale
Time series
time series forecasting
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Summary:Although Transformer-based methods have achieved remarkable performance in the field of long-term series forecasting, they can be computationally expensive and lack the ability to specifically model local features as CNNs. CNN-based methods, such as temporal convolutional network (TCN), utilize convolutional filters to capture local temporal features. However, the intermediate layers of TCN suffer from a limited effective receptive field, which can result in the loss of temporal relations during global feature extraction.To solve the above problems, we propose to combine local features and global correlations to capture the overall view of time series (e.g. fluctuations, trends). To fully exploit the underlying information in the time series, a multi-scale branch structure is adopted to model different potential patterns separately. Each pattern is extracted using a combination of interactive learning convolution and causal frequency enhancement to capture both local features and global correlations. Furthermore, our proposed method,multi-scale local-global feature learning network (MLGN), achieves a time and memory complexity of O ( L ) and consistently achieve state-of-the-art results on six benchmark datasets. In comparision with previous best method Fedformer, MLGN yields 12.98% and 11.38% relative improvements for multivariate and univariate time series, respectively. Our code and data are available on Github at https://github.com/Zero-coder/MLGN .
ISSN:2632-2153
2632-2153
DOI:10.1088/2632-2153/ad1436