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Networked Time-series Prediction with Incomplete Data via Generative Adversarial Network

Published: 28 February 2024 Publication History

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 to 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, and so on. In this article, we study the problem of NETS prediction with incomplete data. We propose networked time series Imputation Generative Adversarial Network (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 Mean Absolute Error by up to 25%.

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  • (2024)Contrastive learning-based multi-view clustering for incomplete multivariate time seriesInformation Fusion10.1016/j.inffus.2024.102812(102812)Online publication date: Nov-2024

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  1. Networked Time-series Prediction with Incomplete Data via Generative Adversarial Network

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      cover image ACM Transactions on Knowledge Discovery from Data
      ACM Transactions on Knowledge Discovery from Data  Volume 18, Issue 5
      June 2024
      699 pages
      EISSN:1556-472X
      DOI:10.1145/3613659
      Issue’s Table of Contents

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 28 February 2024
      Online AM: 05 February 2024
      Accepted: 16 January 2024
      Revised: 28 October 2023
      Received: 12 May 2023
      Published in TKDD Volume 18, Issue 5

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      Author Tags

      1. Networked time series
      2. incomplete data
      3. prediction
      4. imputation

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      • National Natural Science Foundation of China
      • Alibaba Innovative Research (AIR) programme, and the Open Research Project of the State Key Laboratory of Media Convergence and Communication, Communication University of China, China

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      • (2024)Contrastive learning-based multi-view clustering for incomplete multivariate time seriesInformation Fusion10.1016/j.inffus.2024.102812(102812)Online publication date: Nov-2024

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