超越固定变量：通过扁平化方案与时空焦点学习实现扩展变量时间序列预测
Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning
AI总结：
针对新传感器增加导致的变量扩展预测难题，提出灵活时空框架STEV。其通过Flat Scheme展平2D样本解决数据形状不一致，并利用时空焦点学习策略优化数据有限的扩展变量，在仅用5%观测数据时即可媲美全量训练的SOTA模型。
摘要：
多元时间序列预测（MTSF）长期以来一直是一个关键的研究重点。传统上，这些研究假设变量的数量是固定的，但在现实应用中，随着部署新传感器，信息物理系统通常会扩展，从而增加了 MTSF 中的变量。有鉴于此，我们引入了一项新任务：扩展变量时间序列预测（EVTSF）。该任务带来了独特的挑战，具体包括：（1）处理因添加新变量而导致的数据形状不一致问题，以及（2）解决不平衡的时空学习问题，其中由于需要及时操作，扩展变量拥有的观测数据有限。为了应对这些挑战，我们提出了 STEV，一个灵活的时空预测框架。STEV 包含一种新的 Flat Scheme 以解决数据形状不一致的问题，该方案通过沿变量维度展平 2D 样本，将基于图的时空建模架构扩展到 1D 空间，使模型对变量规模不可知，同时仍通过整体图保留动态空间相关性。我们引入了一种新颖的 Spatio-temporal Focal Learning 策略，该策略包含一个负滤波器以解决对比学习与图表示之间的潜在冲突，并以焦点对比损失作为其核心，引导框架专注于优化扩展变量。我们使用三个真实世界数据集对 EVTSF 性能进行了基准测试，并将其与采用为 EVSTF 定制的 SOTA MTSF 模型的三种潜在解决方案进行了比较。实验结果表明，STEV 显著优于其竞争对手，特别是在扩展变量上。值得注意的是，仅使用扩展期 5% 的观测数据，STEV 就能与使用完整观测数据训练的 SOTA MTSF 模型相媲美。对各种扩展策略的进一步探索强调了 STEV 在实际应用中的泛化能力。
英文摘要：
Multivariate Time Series Forecasting (MTSF) has long been a key research focus. Traditionally, these studies assume a fixed number of variables, but in real-world applications, Cyber-Physical Systems often expand as new sensors are deployed, increasing variables in MTSF. In light of this, we introduce a novel task, Expanding-variate Time Series Forecasting (EVTSF). This task presents unique challenges, specifically (1) handling inconsistent data shapes caused by adding new variables, and (2) addressing imbalanced spatio-temporal learning, where expanding variables have limited observed data due to the necessity for timely operation. To address these challenges, we propose STEV, a flexible spatio-temporal forecasting framework. STEV includes a new Flat Scheme to tackle the inconsistent data shape issue, which extends the graph-based spatio-temporal modeling architecture into 1D space by flattening the 2D samples along the variable dimension, making the model variable-scale-agnostic while still preserving dynamic spatial correlations through a holistic graph. We introduce a novel Spatio-temporal Focal Learning strategy that incorporates a negative filter to resolve potential conflicts between contrastive learning and graph representation, and a focal contrastive loss as its core to guide the framework to focus on optimizing the expanding variables. We benchmark EVTSF performance using three real-world datasets and compare it against three potential solutions employing SOTA MTSF models tailored for EVSTF. Experimental results show that STEV significantly outperforms its competitors, particularly on expanding variables. Notably, STEV, with only 5% of observations from the expanding period, is on par with SOTA MTSF models trained with complete observations. Further exploration of various expanding strategies underscores the generalizability of STEV in real-world applications.
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