迈向稳健的现实多变量时间序列预测：一种统一的框架用于依赖性、异步性和缺失性
Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
AI总结：
本文提出ChannelTokenFormer框架，通过统一处理多变量时间序列中的通道依赖、异步采样和缺失问题，提升预测的稳健性和准确性。
摘要：
现实时间序列数据本质上是多变量的，通常表现出复杂的跨通道依赖性。每个通道通常以自己的周期采样，并且由于各种实际和操作限制而容易出现缺失值。这些特性提出了三个基本挑战，涉及通道依赖性、采样异步性和缺失性，所有这些挑战必须同时解决，以在实际环境中实现稳健和可靠的预测。然而，现有架构通常只解决这些挑战中的一部分，并仍然依赖于简化的假设，留下未解决的异步通道采样、测试时缺失块和复杂的跨通道依赖性挑战。为弥补这一差距，我们提出了ChannelTokenFormer，一种基于Transformer的预测框架，具有灵活的架构，旨在显式捕捉跨通道交互，适应通道层面的异步采样，并有效处理缺失值。在公共基准数据集和一个私有现实工业数据集上的广泛实验表明，ChannelTokenFormer在具有挑战性的现实条件下表现出优越的稳健性和准确性。
英文摘要：
Real-world time series data are inherently multivariate, often exhibiting complex inter-channel dependencies. Each channel is typically sampled at its own period and is prone to missing values due to various practical and operational constraints. These characteristics pose three fundamental challenges involving channel dependency, sampling asynchrony, and missingness, all of which must be addressed simultaneously to enable robust and reliable forecasting in practical settings. However, existing architectures typically address only parts of these challenges in isolation and still rely on simplifying assumptions, leaving unresolved the combined challenges of asynchronous channel sampling, test-time missing blocks, and intricate inter-channel dependencies. To bridge this gap, we propose ChannelTokenFormer, a Transformer-based forecasting framework with a flexible architecture designed to explicitly capture cross-channel interactions, accommodate channel-wise asynchronous sampling, and effectively handle missing values. Extensive experiments on public benchmark datasets reflecting practical settings, along with one private real-world industrial dataset, demonstrate the superior robustness and accuracy of ChannelTokenFormer under challenging real-world conditions.
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。
