Scholarlink

Time series forecasting remains a fundamental challenge in data science. We introduce SWIFT, a novel neural architecture that synergistically combines selective state space models (Mamba) with multi-scale dilated convolutions for enhanced time series forecasting. Our approach incorporates: (1) a Selective Temporal State Space module extending Mamba with time series-specific gating; (2) a Multi-Scale Dilated Convolutional Network with adaptive receptive fields; and (3) a Feature Interaction Bridge facilitating cross-pathway information exchange. Experiments on six benchmark datasets demonstrate SWIFT outperforms other common methods, achieving 6.5% average improvement in MSE and 5.8% in MAE across various prediction horizons.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SWIFT: State-Space Wavelet Integrated Forecasting Technology for Enhanced Time Series Prediction

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Wei Li

摘要

Time series forecasting remains a fundamental challenge in data science. We introduce SWIFT, a novel neural architecture that synergistically combines selective state space models (Mamba) with multi-scale dilated convolutions for enhanced time series forecasting. Our approach incorporates: (1) a Selective Temporal State Space module extending Mamba with time series-specific gating; (2) a Multi-Scale Dilated Convolutional Network with adaptive receptive fields; and (3) a Feature Interaction Bridge facilitating cross-pathway information exchange. Experiments on six benchmark datasets demonstrate SWIFT outperforms other common methods, achieving 6.5% average improvement in MSE and 5.8% in MAE across various prediction horizons.