arXiv 2506.130642025-06-20cs.LGstat.ML

CoIFNet：一种面向缺失值多元时间序列预测的统一框架

CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values

School of Computing and Artificial Intelligence, Southwest Jiaotong University（计算机与人工智能学院，西南交通大学）；School of Mathematics, Southwest Jiaotong University（数学学院，西南交通大学）；Institute Carbon Neutrality, Tsinghua University（碳中和研究院，清华大学）；White Rose Grid e-Science Centre, University of Leeds（白玫瑰网格e科学中心，利兹大学）

Kai Tang, Ji Zhang, Hua Meng, Minbo Ma, Qi Xiong, Fengmao Lv, Jie Xu, Tianrui Li

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AI总结：

针对多元时间序列预测中缺失值问题，提出统一插补与预测的CoIFNet框架，通过跨时间步和跨变量融合模块捕获鲁棒时序依赖，在多个基准上超越现有方法，并显著提升效率。

摘要：

多元时间序列预测（MTSF）是一项关键任务，在气象、交通和经济等领域有着广泛的应用。然而，由传感器故障或人为错误导致的普遍缺失值会显著降低预测精度。以往的研究通常采用“先插补后预测”的范式，由于误差累积和两阶段目标不一致，导致预测效果欠佳。为解决这一挑战，我们提出了协作插补-预测网络（CoIFNet），这是一种新颖的框架，将插补和预测统一起来，以在存在缺失值的情况下实现稳健的MTSF。具体而言，CoIFNet将观测值、掩码矩阵和时间戳嵌入作为输入，依次通过跨时间步融合（CTF）和跨变量融合（CVF）模块进行处理，以捕获对缺失值具有鲁棒性的时间依赖性。我们从理论上论证了CoIFNet的学习目标如何改善含缺失值MTSF的性能界限。通过在具有挑战性的MTSF基准上进行的大量实验，我们证明了所提方法在不同缺失数据场景下的有效性和计算效率，例如，在点（块）缺失率为0.6时，CoIFNet比最先进的方法分别高出24.40%（23.81%），同时内存和时间效率分别提高了4.3倍和2.1倍。我们的代码可在 https://github.com/KaiTang-eng/CoIFNet 获取。

英文摘要：

Multivariate time series forecasting (MTSF) is a critical task with broad applications in domains such as meteorology, transportation, and economics. Nevertheless, pervasive missing values caused by sensor failures or human errors significantly degrade forecasting accuracy. Prior efforts usually employ an impute-then-forecast paradigm, leading to suboptimal predictions due to error accumulation and misaligned objectives between the two stages. To address this challenge, we propose the Collaborative Imputation-Forecasting Network (CoIFNet), a novel framework that unifies imputation and forecasting to achieve robust MTSF in the presence of missing values. Specifically, CoIFNet takes the observed values, mask matrix and timestamp embeddings as input, processing them sequentially through the Cross-Timestep Fusion (CTF) and Cross-Variate Fusion (CVF) modules to capture temporal dependencies that are robust to missing values. We provide theoretical justifications on how our CoIFNet learning objective improves the performance bound of MTSF with missing values. Through extensive experiments on challenging MSTF benchmarks, we demonstrate the effectiveness and computational efficiency of our proposed approach across diverse missing-data scenarios, e.g., CoIFNet outperforms the state-of-the-art method by $\underline{\textbf{24.40}}$% ($\underline{\textbf{23.81}}$%) at a point (block) missing rate of 0.6, while improving memory and time efficiency by $\underline{\boldsymbol{4.3\times}}$ and $\underline{\boldsymbol{2.1\times}}$, respectively. Our code is available at: https://github.com/KaiTang-eng/CoIFNet.

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