STTS-EAD: 改进基于时空学习的时间序列预测
STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via
AI总结：
针对多元时间序列预测中异常预处理与训练分离的局限，提出端到端的STTS-EAD方法，将异常检测与时空预测训练集成并交替优化，经多数据集验证可显著提升预测精度。
摘要：
处理异常是多元时间序列预测中的关键预处理步骤。然而，现有将多元时间序列预测的异常预处理与模型训练分离的方法存在显著局限性。具体而言，这些方法在预处理阶段无法利用对识别与时空因素相关的潜在异常至关重要的辅助信息，反而仅依赖数据分布进行异常检测，这可能导致对大量本可对模型训练产生积极贡献的样本处理不当。为解决这一问题，我们提出STTS-EAD，这是一种端到端方法，将异常检测无缝集成到多元时间序列预测的训练过程中，旨在通过嵌入式异常检测（Embedded Anomaly Detection）改进基于时空学习的时间序列预测。我们提出的STTS-EAD利用时空信息进行预测和异常检测，两部分交替执行并相互优化。据我们所知，STTS-EAD是首个在训练阶段集成异常检测与预测任务以提升多元时间序列预测精度的方法。在一个公开股票数据集和两个来自某知名连锁咖啡企业的真实销售数据集上开展的大量实验表明，我们提出的方法能在训练阶段有效处理检测到的异常，从而提升推理阶段的预测性能，且显著优于基线方法。
英文摘要：
Handling anomalies is a critical preprocessing step in multivariate time series prediction. However, existing approaches that separate anomaly preprocessing from model training for multivariate time series prediction encounter significant limitations. Specifically, these methods fail to utilize auxiliary information crucial for identifying latent anomalies associated with spatiotemporal factors during the preprocessing stage. Instead, they rely solely on data distribution for anomaly detection, which can result in the incorrect processing of numerous samples that could otherwise contribute positively to model training. To address this, we propose STTS-EAD, an end-to-end method that seamlessly integrates anomaly detection into the training process of multivariate time series forecasting and aims to improve Spatio-Temporal learning based Time Series prediction via Embedded Anomaly Detection. Our proposed STTS-EAD leverages spatio-temporal information for forecasting and anomaly detection, with the two parts alternately executed and optimized for each other. To the best of our knowledge, STTS-EAD is the first to integrate anomaly detection and forecasting tasks in the training phase for improving the accuracy of multivariate time series forecasting. Extensive experiments on a public stock dataset and two real-world sales datasets from a renowned coffee chain enterprise show that our proposed method can effectively process detected anomalies in the training stage to improve forecasting performance in the inference stage and significantly outperform baselines.
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。
