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.

本文提出了一种模块化和可解释的预测框架 SCNN，可以分离时间序列数据中的结构和异构组成部分，并分别预测其演变。在三个实际数据集上的实验证明了 SCNN 可以胜过现有模型并具有更好的可追溯性和可预测性。

学习结构组件：面向模块化可解释的多元时间序列预测

Learning Structured Components: Towards Modular and Interpretable  Multivariate Time Series Forecasting

Cellular traffic prediction is an indispensable part for intelligent
telecommunication networks. Nevertheless, due to the frequent user mobility and
complex network scheduling mechanisms, cellular traffic often inherits
complicated spatial-temporal patterns, making the prediction incredibly
challenging. Although recent advanced algorithms such as graph-based prediction
approaches have been proposed, they frequently model spatial dependencies based
on static or dynamic graphs and neglect the coexisting multiple spatial
correlations induced by traffic generation. Meanwhile, some works lack the
consideration of the diverse cellular traffic patterns, result in suboptimal
prediction results. In this paper, we propose a novel deep learning network
architecture, Adaptive Hybrid Spatial-Temporal Graph Neural Network (AHSTGNN),
to tackle the cellular traffic prediction problem. First, we apply adaptive
hybrid graph learning to learn the compound spatial correlations among cell
towers. Second, we implement a Temporal Convolution Module with multi-periodic
temporal data input to capture the nonlinear temporal dependencies. In
addition, we introduce an extra Spatial-Temporal Adaptive Module to conquer the
heterogeneity lying in cell towers. Our experiments on two real-world cellular
traffic datasets show AHSTGNN outperforms the state-of-the-art by a significant
margin, illustrating the superior scalability of our method for
spatial-temporal cellular traffic prediction.

本研究提出了一种名为自适应混合时空图神经网络 (AHSTGNN) 的网络架构，用于解决基于时空模式的移动通信网络中的流量预测问题。实验结果表明，AHSTGNN 模型在两个真实的移动通信数据集上性能优于现有的预测方法。

自适应混合空间 - 时间图神经网络用于移动通讯流量预测

Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction

Crime prediction is crucial for public safety and resource optimization, yet
is very challenging due to two aspects: i) the dynamics of criminal patterns
across time and space, crime events are distributed unevenly on both spatial
and temporal domains; ii) time-evolving dependencies between different types of
crimes (e.g., Theft, Robbery, Assault, Damage) which reveal fine-grained
semantics of crimes. To tackle these challenges, we propose Spatial-Temporal
Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime
spatial-temporal patterns as well as the underlying category-wise crime
semantic relationships. In specific, to handle spatial-temporal dynamics under
the long-range and global context, we design a graph-structured message passing
architecture with the integration of the hypergraph learning paradigm. To
capture category-wise crime heterogeneous relations in a dynamic environment,
we introduce a multi-channel routing mechanism to learn the time-evolving
structural dependency across crime types. We conduct extensive experiments on
two real-world datasets, showing that our proposed ST-SHN framework can
significantly improve the prediction performance as compared to various
state-of-the-art baselines. The source code is available at:
this https URL.

本文提出了一个能够共同编码复杂犯罪空间和时间模式以及不同类别犯罪语义关系的空间 - 时间序列超图网络，该网络采用超图学习范例，设计图结构信息传递架构，并引入多通道路由机制。通过实验证明了该方法的较强预测性能。