Within the field of complicated multivariate time series forecasting (TSF), popular techniques frequently rely on intricate deep learning architectures, ranging from transformer-based designs to recurrent neural networks. However, recent findings suggest that simple Linear models can surpass sophisticated constructs on diverse datasets. These models directly map observation to multiple future time steps, thereby minimizing error accumulation in iterative multi-step prediction. Yet, these models fail to incorporate spatial and temporal information within the data, which is critical for capturing patterns and dependencies that drive insightful predictions. This oversight often leads to performance bottlenecks, especially under specific sequence lengths and dataset conditions, preventing their universal application. In response, we introduce the SpatioTemporal-Linear (STL) framework. STL seamlessly integrates time-embedded and spatially-informed bypasses to augment the Linear-based architecture. These extra routes offer a more robust and refined regression to the data, particularly when the amount of observation is limited and the capacity of simple linear layers to capture dependencies declines. Empirical evidence highlights STL's prowess, outpacing both Linear and Transformer benchmarks across varied observation and prediction durations and datasets. Such robustness accentuates its suitability across a spectrum of applications, including but not limited to, traffic trajectory and rare disease progression forecasting. Through this discourse, we not only validate the STL's distinctive capacities to become a more general paradigm in multivariate time-series prediction using deep-learning techniques but also stress the need to tackle data-scarce prediction scenarios for universal application. Code will be made available.

翻译：在复杂的多变量时间序列预测领域，主流技术常依赖复杂的深度学习架构，从基于Transformer的设计到循环神经网络。然而，近期研究发现，简单的线性模型能够在多种数据集上超越复杂结构。这类模型直接将观测值映射到多个未来时间步，从而最小化迭代多步预测中的误差累积。然而，现有模型未能有效整合数据中的时空信息，而这些信息对于捕捉驱动洞察性预测的模式与依赖关系至关重要。这一缺陷常导致性能瓶颈，尤其在特定序列长度和数据集条件下，阻碍了其通用应用。为此，我们提出时空线性框架（STL）。STL通过无缝集成时间嵌入与空间感知旁路，增强基于线性模型的架构。这些额外路径在观测数据有限且简单线性层捕获依赖能力下降时，为数据提供了更稳健精细的回归。实验证据表明，STL在多样化的观测与预测时长及数据集上均优于线性模型及Transformer基准。这种鲁棒性突显了其在广泛场景中的适用性，包括但不限于交通轨迹预测与罕见疾病进展预测。通过本研究，我们不仅验证了STL作为深度学习技术在多变量时间序列预测中成为更通用范式的独特能力，还强调了需解决数据稀缺预测场景以实现通用应用的必要性。相关代码将公开提供。