时间与频率的桥梁：一种联合建模框架用于不规则多变量时间序列预测
Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting
AI总结：
TFMixer通过联合时频建模框架解决不规则多变量时间序列预测中的非均匀采样和变量异步性问题，结合全局频域模块和局部时间模块实现高精度预测。
摘要：
不规则多变量时间序列预测（IMTSF）因非均匀采样和变量异步性而具有挑战性。这些不规则性违反了标准模型的等距假设，阻碍了局部时间建模，并使经典频域方法无法捕捉到全局周期结构。为了解决这一挑战，我们提出TFMixer，一种用于IMTS预测的联合时频建模框架。具体而言，TFMixer包含一个全局频域模块，该模块使用可学习的非均匀离散傅里叶变换（NUDFT）直接从不规则时间戳中提取频谱表示。同时，局部时间模块引入了基于查询的块混合机制，以自适应地聚合信息丰富的时间块并缓解信息密度不平衡。最后，TFMixer融合时域和频域表示以生成预测，并进一步利用逆NUDFT进行显式季节外推。在真实世界数据集上的广泛实验表明了TFMixer的最先进的性能。
英文摘要：
Irregular multivariate time series forecasting (IMTSF) is challenging due to non-uniform sampling and variable asynchronicity. These irregularities violate the equidistant assumptions of standard models, hindering local temporal modeling and rendering classical frequency-domain methods ineffective for capturing global periodic structures. To address this challenge, we propose TFMixer, a joint time-frequency modeling framework for IMTS forecasting. Specifically, TFMixer incorporates a Global Frequency Module that employs a learnable Non-Uniform Discrete Fourier Transform (NUDFT) to directly extract spectral representations from irregular timestamps. In parallel, the Local Time Module introduces a query-based patch mixing mechanism to adaptively aggregate informative temporal patches and alleviate information density imbalance. Finally, TFMixer fuses the time-domain and frequency-domain representations to generate forecasts and further leverages inverse NUDFT for explicit seasonal extrapolation. Extensive experiments on real-world datasets demonstrate the state--of-the-art performance of TFMixer.
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。
