Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many real-world applications. The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns. In this paper, we develop a modular and interpretable forecasting framework, which seeks to individually model each component of the spatial-temporal patterns. We name this framework SCNN, short for Structured Component-based Neural Network. SCNN works with a pre-defined generative process of MTS, which arithmetically characterizes the latent structure of the spatial-temporal patterns. In line with its reverse process, SCNN decouples MTS data into structured and heterogeneous components and then respectively extrapolates the evolution of these components, the dynamics of which is more traceable and predictable than the original MTS. Extensive experiments are conducted to demonstrate that SCNN can achieve superior performance over state-of-the-art models on three real-world datasets. Additionally, we examine SCNN with different configurations and perform in-depth analyses of the properties of SCNN.

翻译：多变量时间序列预测是许多实际应用中至关重要且基础的问题。其核心挑战在于如何有效建模复杂的时空模式。本文提出了一种模块化且可解释的预测框架，旨在分别对时空模式的各个组件进行建模，命名为SCNN（结构化组件神经网络）。SCNN基于预设的多变量时间序列生成过程，该过程以算术方式刻画了时空模式的潜在结构。遵循其逆过程，SCNN将多变量时间序列数据解耦为结构化异构组件，并分别推演这些组件的演化——相较于原始多变量时间序列，其动态特性更具可追踪性与可预测性。通过在三组真实数据集上的广泛实验证明，SCNN的性能优于当前最先进模型。此外，我们考察了不同配置下的SCNN，并对其特性进行了深入分析。