Conv-like Scale-Fusion Time Series Transformer: A Multi-Scale Representation for Variable-Length Long Time Series

• URL: http://arxiv.org/abs/2509.17845v1
• Date: Mon, 22 Sep 2025 14:37:59 GMT
• Title: Conv-like Scale-Fusion Time Series Transformer: A Multi-Scale Representation for Variable-Length Long Time Series
• Authors: Kai Zhang, Siming Sun, Zhengyu Fan, Qinmin Yang, Xuejun Jiang,
• Abstract summary: Transformer-based models have advanced time series tasks, but struggle with feature redundancy and limited generalization capabilities.<n>We propose a Multi-Scale Representation Learning Framework based on a Conv-like ScaleFusion Transformer.<n>Our framework achieves superior feature independence, reduced redundancy, and better performance in forecasting and classification tasks compared to state-of-the-art methods.
• Score: 10.93942806756288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series analysis faces significant challenges in handling variable-length data and achieving robust generalization. While Transformer-based models have advanced time series tasks, they often struggle with feature redundancy and limited generalization capabilities. Drawing inspiration from classical CNN architectures' pyramidal structure, we propose a Multi-Scale Representation Learning Framework based on a Conv-like ScaleFusion Transformer. Our approach introduces a temporal convolution-like structure that combines patching operations with multi-head attention, enabling progressive temporal dimension compression and feature channel expansion. We further develop a novel cross-scale attention mechanism for effective feature fusion across different temporal scales, along with a log-space normalization method for variable-length sequences. Extensive experiments demonstrate that our framework achieves superior feature independence, reduced redundancy, and better performance in forecasting and classification tasks compared to state-of-the-art methods.

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