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DSPNet: A transformer network based on multi-scale feature modeling and patch representation for long-term multivariate time series forecasting

artificial-intelligence
data-mining-and-machine-learning
data-science
neural-networks

Abstract

Long-term time series forecasting plays a pivotal role in key application scenarios like climate prediction, electricity load forecasting, and traffic flow assessment. Conventional single-architecture models—including Transformers, CNNs, and MLPs—frequently face challenges in comprehensively capturing intricate temporal patterns. To address this limitation, we put forward DSPNet, a hybrid framework that combines multi-scale feature modeling with patch-based sequence representation. Specifically, multi-scale modeling is designed to extract trend and seasonal components, patch operations focus on capturing local patterns, and the Transformer serves as the backbone to model global dependencies. Through extensive experiments conducted on seven real-world benchmark datasets, it is demonstrated that DSPNet achieves consistent performance superior to that of state-of-the-art models. Meanwhile, ablation studies further validate the significance of each component within the framework. Overall, DSPNet establishes a new paradigm for realizing accurate long-term time series forecasting. The implementation of DSPNet is publicly available at https://github.com/jk16171216/DSPNet.
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