TimePro：具有变量和时间感知超状态的高效多变量长期时间序列预测
TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state
AI总结：
针对多延迟问题，提出基于Mamba的TimePro模型，通过构建变量和时间感知超状态，捕捉变量关系与时间信息，在八个基准上实现高效准确预测。
摘要：
在长期时间序列预测中，不同变量往往在不同的时间间隔内影响目标变量，这一挑战被称为多延迟问题。传统模型通常统一处理所有变量或时间点，这限制了它们捕捉复杂变量关系和获得非平凡时间表示的能力。为了解决这个问题，我们提出了TimePro，一种创新的基于Mamba的模型，它构建了变量和时间感知的超状态。与仅在变量或时间维度上传递普通状态的传统方法不同，TimePro保留了每个变量标记的细粒度时间特征，并自适应地选择关注的时间点来调整普通状态。重建的超状态能够感知变量关系和显著的时间信息，这有助于模型进行准确预测。在实验中，TimePro在八个真实世界的长期预测基准上表现优异，并具有令人满意的线性复杂度。代码可在https://github.com/xwmaxwma/TimePro获取。
英文摘要：
In long-term time series forecasting, different variables often influence the target variable over distinct time intervals, a challenge known as the multi-delay issue. Traditional models typically process all variables or time points uniformly, which limits their ability to capture complex variable relationships and obtain non-trivial time representations. To address this issue, we propose TimePro, an innovative Mamba-based model that constructs variate- and time-aware hyper-states. Unlike conventional approaches that merely transfer plain states across variable or time dimensions, TimePro preserves the fine-grained temporal features of each variate token and adaptively selects the focused time points to tune the plain state. The reconstructed hyper-state can perceive both variable relationships and salient temporal information, which helps the model make accurate forecasting. In experiments, TimePro performs competitively on eight real-world long-term forecasting benchmarks with satisfactory linear complexity. Code is available at https://github.com/xwmaxwma/TimePro.
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。
