Stecformer：用于多元长期时间序列预测的时空编码级联Transformer
Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting
AI总结：
本文提出Stecformer模型，通过半自适应图的时空编码提取器和级联解码预测器CDP，解决多元长期时间序列预测中空间特征提取不足和不同预测周期一致性问题，在五个基准数据集上取得与最先进方法相当的性能。
摘要：
多元长期时间序列预测在能源消耗和天气预报等许多领域具有重要应用。随着基于Transformer方法的发展，多元长期时间序列预测的性能得到了显著提升，然而，基于Transformer模型中空间特征提取的研究较少，且由于时间跨度较大，不同预测周期之间的一致性也不令人满意。在本工作中，我们提出了一套完整的解决方案，从特征提取和目标预测两方面解决这些问题。在特征提取方面，我们设计了一个高效的时空编码提取器，其中包含一个半自适应图，以获取充分的时空信息。在预测方面，我们提出了级联解码预测器（CDP），以增强不同时间间隔之间的相关性，该组件也可作为通用组件用于提升基于Transformer方法的性能。所提出的方法称为时空编码级联Transformer（Stecformer），在五个基准数据集上相较于基线模型取得了显著差距，并与基于Transformer方法的最新性能相当。我们希望我们的尝试能够在未来成为多元长期时间序列预测中的常规配置。
英文摘要：
Multivariate long-term time series forecasting is of great application across many domains, such as energy consumption and weather forecasting. With the development of transformer-based methods, the performance of multivariate long-term time series forecasting has been significantly improved, however, the study of spatial features extracting in transformer-based model is rare and the consistency of different prediction periods is unsatisfactory due to the large span. In this work, we propose a complete solution to address these problems in terms of feature extraction and target prediction. For extraction, we design an efficient spatio-temporal encoding extractor including a semi-adaptive graph to acquire sufficient spatio-temporal information. For prediction, we propose a Cascaded Decoding Predictor (CDP) to strengthen the correlation between different intervals, which can also be utilized as a generic component to improve the performance of transformer-based methods. The proposed method, termed as Spatio-temporal Encoding Cascaded Transformer (Stecformer), achieving a notable gap over the baseline model and is comparable with the state-of-the-art performance of transformer-based methods on five benchmark datasets. We hope our attempt will serve as a regular configuration in multivariate long-term time series forecasting in the future.
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。
