释放线性网络在不规则多元时间序列预测中的潜力
Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting
AI总结：
本文提出AiT模型，通过自适应线性网络动态调整权重解决不规则多元时间序列的序列内不一致性，并结合Transformer模块捕获变量相关性，在四个基准数据集上准确率提升11%、运行时间减少52%。
摘要：
时间序列预测在金融、交通、能源、医疗和气候等多个行业具有重要意义。尽管线性网络因计算成本低且能有效建模时间依赖性而被广泛使用，但现有研究多集中于规则采样且完全观测的多元时间序列。然而，实际中常遇到具有可变采样间隔和缺失值的不规则多元时间序列。此类数据固有的序列内不一致性和序列间异步性，阻碍了依赖静态权重的传统线性网络的有效建模与预测。为应对这些挑战，本文提出名为AiT的新模型。AiT利用自适应线性网络，能根据观测时间点动态调整权重以解决序列内不一致性，从而提升时间依赖性建模的准确性。此外，通过在变量语义嵌入上引入Transformer模块，AiT高效捕获变量相关性，规避了序列间异步性的挑战。在四个基准数据集上的综合实验证明了AiT的优越性，与现有最先进方法相比，预测准确率提高11%，运行时间减少52%。
英文摘要：
Time series forecasting holds significant importance across various industries, including finance, transportation, energy, healthcare, and climate. Despite the widespread use of linear networks due to their low computational cost and effectiveness in modeling temporal dependencies, most existing research has concentrated on regularly sampled and fully observed multivariate time series. However, in practice, we frequently encounter irregular multivariate time series characterized by variable sampling intervals and missing values. The inherent intra-series inconsistency and inter-series asynchrony in such data hinder effective modeling and forecasting with traditional linear networks relying on static weights. To tackle these challenges, this paper introduces a novel model named AiT. AiT utilizes an adaptive linear network capable of dynamically adjusting weights according to observation time points to address intra-series inconsistency, thereby enhancing the accuracy of temporal dependencies modeling. Furthermore, by incorporating the Transformer module on variable semantics embeddings, AiT efficiently captures variable correlations, avoiding the challenge of inter-series asynchrony. Comprehensive experiments across four benchmark datasets demonstrate the superiority of AiT, improving prediction accuracy by 11% and decreasing runtime by 52% compared to existing state-of-the-art methods.
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。
