In recent work on time-series prediction, Transformers and even large language models have garnered significant attention due to their strong capabilities in sequence modeling. However, in practical deployments, time-series prediction often requires operation in resource-constrained environments, such as edge devices, which are unable to handle the computational overhead of large models. To address such scenarios, some lightweight models have been proposed, but they exhibit poor performance on non-stationary sequences. In this paper, we propose $\textit{SWIFT}$, a lightweight model that is not only powerful, but also efficient in deployment and inference for Long-term Time Series Forecasting (LTSF). Our model is based on three key points: (i) Utilizing wavelet transform to perform lossless downsampling of time series. (ii) Achieving cross-band information fusion with a learnable filter. (iii) Using only one shared linear layer or one shallow MLP for sub-series' mapping. We conduct comprehensive experiments, and the results show that $\textit{SWIFT}$ achieves state-of-the-art (SOTA) performance on multiple datasets, offering a promising method for edge computing and deployment in this task. Moreover, it is noteworthy that the number of parameters in $\textit{SWIFT-Linear}$ is only 25\% of what it would be with a single-layer linear model for time-domain prediction. Our code is available at https://github.com/LancelotXWX/SWIFT.

翻译：近期时间序列预测研究中，Transformer乃至大语言模型因其在序列建模方面的强大能力而备受关注。然而在实际部署中，时间序列预测常需在资源受限的环境（如边缘设备）中运行，这些环境无法承受大模型的计算开销。针对此类场景，已有一些轻量级模型被提出，但它们在非平稳序列上表现不佳。本文提出$\textit{SWIFT}$——一种轻量级模型，不仅性能强大，而且在长期时间序列预测（LTSF）的部署与推理中具有高效性。我们的模型基于三个关键点：（i）利用小波变换对时间序列进行无损下采样；（ii）通过可学习滤波器实现跨频带信息融合；（iii）仅使用一个共享线性层或一个浅层MLP完成子序列映射。我们进行了全面的实验，结果表明$\textit{SWIFT}$在多个数据集上达到了最先进的性能，为该任务的边缘计算与部署提供了有前景的方法。值得注意的是，$\textit{SWIFT-Linear}$的参数量仅为时域预测中单层线性模型所需参数量的25%。代码公开于https://github.com/LancelotXWX/SWIFT。