research-article

DAMR: Dynamic Adjacency Matrix Representation Learning for Multivariate Time Series Imputation

Authors:

Patricia J. Riddle,

Katerina Taskova,

Lianyan LiAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 2

Article No.: 188, Pages 1 - 25

https://doi.org/10.1145/3589333

Published: 20 June 2023 Publication History

Abstract

Missing data imputation for location-based sensor data has attracted much attention in recent years. The state-of-the-art imputation methods based on graph neural networks have a priori assumption that the spatial correlations between sensor locations are static. However, real-world data sets often exhibit dynamic spatial correlations. This paper proposes a novel approach to capturing the dynamics of spatial correlations between geographical locations as a composition of the constant, long-term trends and periodic patterns. To this end, we design a new method called Dynamic Adjacency Matrix Representation (DAMR) that extracts various dynamic patterns of spatial correlations and represents them as adjacency matrices. The adjacency matrices are then aggregated and fed into a well-designed graph representation learning layer for predicting the missing values. Through extensive experiments on six real-world data sets, we demonstrate that DAMR reduces the MAE by up to 19.4% compared with the state-of-the-art methods for the missing value imputation task

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Cited By

Yu CWang FShao ZQian TZhang ZWei WXu YBaeza-Yates RBonchi F(2024)GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable MissingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672055(3989-4000)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672055
Obata KKawabata KMatsubara YSakurai YBaeza-Yates RBonchi F(2024)Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time SeriesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671760(2296-2306)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671760

Index Terms

DAMR: Dynamic Adjacency Matrix Representation Learning for Multivariate Time Series Imputation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 2

PACMMOD

June 2023

2310 pages

EISSN:2836-6573

DOI:10.1145/3605748

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2023 ACM.

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Publication History

Published: 20 June 2023

Published in PACMMOD Volume 1, Issue 2

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Cited By

Yu CWang FShao ZQian TZhang ZWei WXu YBaeza-Yates RBonchi F(2024)GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable MissingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672055(3989-4000)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672055
Obata KKawabata KMatsubara YSakurai YBaeza-Yates RBonchi F(2024)Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time SeriesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671760(2296-2306)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671760

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