Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network

TL;DR

This paper introduces NETS-ImpGAN, a deep learning framework for predicting networked time series with incomplete data, utilizing generative adversarial networks and attention mechanisms to improve accuracy over existing methods.

Contribution

The paper presents a novel GAN-based approach combined with graph temporal attention networks for accurate prediction of NETS with missing data, a challenge in real-world applications.

Findings

NETS-ImpGAN outperforms existing methods in MAE reduction.

The framework effectively handles missing data in both historical and future values.

Experiments on four real-world datasets demonstrate robustness across different missing patterns.

Abstract

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks, which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%.