FilterTS：面向多元时间序列预测的全面频率滤波
FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting
AI总结：
FilterTS通过动态跨变量滤波和静态全局滤波在频率域建模多元时间序列，将时域卷积转为频域乘法，在八个数据集上显著提升预测精度与计算效率。
摘要：
多元时间序列预测在各个行业中至关重要，准确提取复杂的周期性和趋势成分可以显著提升预测性能。然而，现有模型往往难以捕捉这些复杂模式。为应对这些挑战，我们提出了FilterTS，这是一种利用基于频率域的专门滤波技术的新型预测模型。FilterTS引入了一个动态跨变量滤波模块，这是一项关键创新，能够动态利用其他变量作为滤波器，以提取并强化多元时间序列中跨变量的共享变量频率成分。此外，一个静态全局滤波模块捕获在整个训练集中识别出的稳定频率成分。而且，该模型构建于频率域中，将时域卷积转换为频率域乘法运算，以提高计算效率。在八个真实世界数据集上的大量实验结果表明，FilterTS在预测精度和计算效率方面显著优于现有方法。
英文摘要：
Multivariate time series forecasting is crucial across various industries, where accurate extraction of complex periodic and trend components can significantly enhance prediction performance. However, existing models often struggle to capture these intricate patterns. To address these challenges, we propose FilterTS, a novel forecasting model that utilizes specialized filtering techniques based on the frequency domain. FilterTS introduces a Dynamic Cross-Variable Filtering Module, a key innovation that dynamically leverages other variables as filters to extract and reinforce shared variable frequency components across variables in multivariate time series. Additionally, a Static Global Filtering Module captures stable frequency components, identified throughout the entire training set. Moreover, the model is built in the frequency domain, converting time-domain convolutions into frequency-domain multiplicative operations to enhance computational efficiency. Extensive experimental results on eight real-world datasets have demonstrated that FilterTS significantly outperforms existing methods in terms of prediction accuracy and computational efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
