[Paper Review] Modeling Complex Spatial Patterns with Temporal Features via Heterogenous Graph Embedding Networks.

Yida Huang, Haoyan Xu+5 more|arXiv (Cornell University)|Aug 19, 2020

Advanced Graph Neural Networks27 references1 citations

TL;DR

This paper proposes MTHetGNN, a novel end-to-end deep learning model that jointly models dynamic and static relations among multivariate time series (MTS) variables and their temporal dependencies using heterogeneous graph neural networks. By integrating relation embedding modules and multi-scale CNNs for node feature learning, MTHetGNN achieves state-of-the-art performance on three real-world MTS forecasting benchmarks.

ABSTRACT

Multivariate time series (MTS) forecasting is an important problem in many fields. Accurate forecasting results can effectively help decision-making. Variables in MTS have rich relations among each other and the value of each variable in MTS depends both on its historical values and on other variables. These rich relations can be static and predictable or dynamic and latent. Existing methods do not incorporate these rich relational information into modeling or only model certain relation among MTS variables. To jointly model rich relations among variables and temporal dependencies within the time series, a novel end-to-end deep learning model, termed Multivariate Time Series Forecasting via Heterogenous Graph Neural Networks (MTHetGNN) is proposed in this paper. To characterize rich relations among variables, a relation embedding module is introduced in our model, where each variable is regarded as a graph node and each type of edge represents a specific relationship among variables or one specific dynamic update strategy to model the latent dependency among variables. In addition, convolutional neural network (CNN) filters with different perception scales are used for time series feature extraction, which is used to generate the feature of each node. Finally, heterogenous graph neural networks are adopted to handle the complex structural information generated by temporal embedding module and relation embedding module. Three benchmark datasets from the real world are used to evaluate the proposed MTHetGNN and the comprehensive experiments show that MTHetGNN achieves state-of-the-art results in MTS forecasting task.

Motivation & Objective

• To address the limitation of existing MTS forecasting models that fail to jointly model rich, dynamic, and static relations among variables.
• To incorporate both explicit and latent dependencies among MTS variables into a unified deep learning framework.
• To improve forecasting accuracy by leveraging heterogeneous graph structures that encode diverse types of relational information.
• To model temporal dependencies effectively using multi-scale CNN filters for time series feature extraction.
• To develop an end-to-end framework that integrates relational and temporal modeling for superior forecasting performance.

Proposed method

• Each variable in the MTS is modeled as a node in a heterogeneous graph, with distinct edge types representing different kinds of relationships or dynamic update strategies.
• A relation embedding module learns representations of these diverse relational types, enabling the model to capture both static and latent dependencies among variables.
• Multi-scale CNN filters are applied to the time series of each variable to extract temporal features and generate initial node representations.
• Heterogeneous graph neural networks (H-GNNs) are employed to aggregate and propagate information across the graph, capturing complex structural dependencies from both temporal and relational modules.
• The model is trained end-to-end to optimize forecasting performance on future time steps.
• The integration of temporal feature extraction and relational graph learning enables joint modeling of inter-variable relations and temporal dynamics.

Experimental results

Research questions

• RQ1 How can dynamic and static relations among MTS variables be effectively modeled within a deep learning framework?
• RQ2 To what extent can heterogeneous graph neural networks improve MTS forecasting by capturing complex relational structures?
• RQ3 Can multi-scale temporal feature extraction enhance the representation of individual time series variables in a relational context?
• RQ4 How does the joint modeling of temporal dependencies and inter-variable relations impact forecasting accuracy compared to existing methods?
• RQ5 Does the proposed MTHetGNN model achieve state-of-the-art performance across diverse real-world MTS datasets?

Key findings

• MTHetGNN achieves state-of-the-art performance on three real-world multivariate time series forecasting benchmarks.
• The integration of heterogeneous graph neural networks enables effective modeling of both static and dynamic relations among MTS variables.
• The use of multi-scale CNN filters improves temporal feature extraction, contributing to enhanced forecasting accuracy.
• The relation embedding module successfully captures diverse types of dependencies, including latent and dynamic ones, across variables.
• Empirical results demonstrate that jointly modeling temporal and relational structures leads to significant performance gains over existing methods.
• The model's end-to-end training strategy effectively learns representations that generalize well across different MTS datasets.