Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network
This addresses a practical issue in applications like intelligent transportation and smart grids where sensor data is often missing, though it is an incremental improvement over existing methods.
The paper tackles the problem of predicting networked time series with incomplete data, proposing NETS-ImpGAN, a deep learning framework that reduces MAE by up to 25% compared to existing methods.
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%.