G-TIGRE: A new generative framework for Multivariate Time Series Imputation By Graph Neural Networks

Javier Solís-García, Belén Vega-Márquez, Juan A. Nepomuceno, Isabel A. Nepomuceno-Chamorro

Keywords: generative adversarial network, graph neural network, time series, imputation

TL;DR: We introduce a novel framework that combines Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) to address the challenge of missing data in multivariate time series

Abstract: The persistent challenge of handling missing values in multivariate time series (MTS) data demands precise solutions to avoid potential pitfalls in real-world applications. Conventional imputation methods often struggle to capture effective spatio-temporal representations of such data, failing to exploit its intrinsic temporal nature and intricate inter-variable relationships. In recent years, deep learning-based imputation methods have gained popularity. However, they often lack dedicated structures and models specifically designed to address this unique challenge. In response to these challenges, we introduce a novel framework called G-TIGRE, which synergistically leverages the capabilities of two prominent research streams in this field: Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs). GANs excel at effectively modeling data distributions, while GNNs demonstrate remarkable proficiency in extracting spatio-temporal features from data. By integrating these two techniques, which have not previously been explored together in this domain, G-TIGRE addresses several critical issues, including the elimination of the need to make assumptions about data stationarity, the ability to train with incomplete data, and the enhancement of spatio-temporal representation learning. Through extensive experiments conducted on a diverse benchmark of state-of-the-art methods, we establish that G-TIGRE achieves competitive performance, closely rivaling the top-performing models. Furthermore, an in-depth ablation study sheds light on the unique contributions of each component within G-TIGRE, elucidating its effectiveness in MTS imputation. This work introduces an exciting shift in addressing the persistent challenges of missing data in multivariate time series, with far-reaching implications across various domains.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 4147