Multivariate time series (MTS) forecasting involves predicting future time
series data based on historical observations. Existing research primarily
emphasizes the development of complex spatial-temporal models that capture
spatial dependencies and temporal correlations among time series variables
explicitly. However, recent advances have been impeded by challenges relating
to data scarcity and model robustness. To address these issues, we propose
Spatial-Temporal Masked Autoencoders (STMAE), an MTS forecasting framework that
leverages masked autoencoders to enhance the performance of spatial-temporal
baseline models. STMAE consists of two learning stages. In the pretraining
stage, an encoder-decoder architecture is employed. The encoder processes the
partially visible MTS data produced by a novel dual-masking strategy, including
biased random walk-based spatial masking and patch-based temporal masking.
Subsequently, the decoders aim to reconstruct the masked counterparts from both
spatial and temporal perspectives. The pretraining stage establishes a
challenging pretext task, compelling the encoder to learn robust
spatial-temporal patterns. In the fine-tuning stage, the pretrained encoder is
retained, and the original decoder from existing spatial-temporal models is
appended for forecasting. Extensive experiments are conducted on multiple MTS
benchmarks. The promising results demonstrate that integrating STMAE into
various spatial-temporal models can largely enhance their MTS forecasting
capability.

该研究提出了一种基于空间 - 时间蒙面自编码器（STMAE）的多元时间序列（MTS）预测框架，利用蒙面自编码器来增强空间 - 时间基线模型的性能，并通过在多个 MTS 基准上进行广泛实验来证明其在提高 MTS 预测能力方面的潜力。

揭示空间时间遮挡自编码器在多变量时间序列预测中的威力

Revealing the Power of Spatial-Temporal Masked Autoencoders in  Multivariate Time Series Forecasting

Federated learning involves training statistical models over edge devices
such as mobile phones such that the training data is kept local. Federated
Learning (FL) can serve as an ideal candidate for training spatial temporal
models that rely on heterogeneous and potentially massive numbers of
participants while preserving the privacy of highly sensitive location data.
However, there are unique challenges involved with transitioning existing
spatial temporal models to decentralized learning. In this survey paper, we
review the existing literature that has proposed FL-based models for predicting
human mobility, traffic prediction, community detection, location-based
recommendation systems, and other spatial-temporal tasks. We describe the
metrics and datasets these works have been using and create a baseline of these
approaches in comparison to the centralized settings. Finally, we discuss the
challenges of applying spatial-temporal models in a decentralized setting and
by highlighting the gaps in the literature we provide a road map and
opportunities for the research community.

本文综述了现有文献中所提出的基于联邦学习的预测人类移动性、交通预测、社区探测、基于位置的推荐系统和其他时空任务的模型，阐述了这些模型所使用的指标和数据集，并与中心化模型进行比较。最后，讨论了在分散式学习中应用时空模型所面临的挑战，并提出了未来研究的机会和方向。