XLinear通过频域增强与跨滤波机制，提升MLP模型对长序列预测的性能。
XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting
- 将时序分解为趋势与季节成分，分别用频域注意力和跨滤波块处理
- 在多个数据集上超越现有MLP模型，实现顶尖长程依赖捕捉能力
- 适合需要轻量级且抗噪能力强的长期预测场景
时间序列预测广泛应用于多个领域。基于MLP的预测器相较于Transformer类模型更具抗噪鲁棒性，但难以捕捉复杂特征，尤其在长程依赖建模方面存在局限。为此，本文提出XLinear，一种面向长程预测的MLP基预测器。首先将时间序列分解为趋势与季节成分：针对含长程特性的趋势分量，设计频域操作的增强频域注意力（EFA）以捕捉长期依赖；针对季节分量，引入跨滤波块（CrossFilter Block），保持模型对噪声的鲁棒性，避免注意力机制常见的低鲁棒性问题。实验表明，XLinear在测试数据集上达到当前最优性能，在维持MLP轻量架构与高鲁棒性的同时，显著优于其他基于MLP的模型在长程依赖建模上的表现。
原文摘要 · Abstract (English)
Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex features, resulting in limitations on capturing long-range dependencies. To address this challenge, we propose XLinear, an MLP-based forecaster for long-range forecasting. Firstly, we decompose the time series into trend and seasonal components. For the trend component which contains long-range characteristics, we design Enhanced Frequency Attention (EFA) to capture long-term dependencies by leveraging frequency-domain operations. Additionally, a CrossFilter Block is proposed for the seasonal component to maintain the model's robustness to noise, avoiding the problems of low robustness often caused by attention mechanisms. Experimental results demonstrate that XLinear achieves state-of-the-art performance on test datasets. While keeping the lightweight architecture and high robustness of MLP-based models, our forecaster outperforms other MLP-based forecasters in capturing long-range dependencies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。