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Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders.

Dingsu Wang1, Yuchen Yan1, Ruizhong Qiu1

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Summary
This summary is machine-generated.

This study introduces PoGeVon, a novel method for imputing missing data in networked time series (NTS). PoGeVon effectively handles dynamic graph structures and missing values in both features and graph topology.

Keywords:
Networked time seriesimputationnode positional embeddingsrandom walk with restartvariational autoencoders

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Area of Science:

  • Data Science
  • Machine Learning
  • Network Science

Background:

  • Multivariate time series (MTS) imputation is crucial for data analysis.
  • Existing methods often neglect dynamic graph structures or assume complete graph information.
  • Networked time series (NTS) present unique challenges due to changing graph topologies and missing edges.

Purpose of the Study:

  • To address limitations in current MTS imputation methods for NTS.
  • To propose a novel model capable of imputing missing values in both time series features and graph structures of NTS.
  • To develop a method that leverages dynamic graph information for more accurate imputation.

Main Methods:

  • Defined the problem of imputation over NTS with missing features and graph structures.
  • Developed the PoGeVon model, utilizing a variational autoencoder (VAE) for imputation.
  • Introduced a novel node position embedding based on random walk with restart (RWR) for enhanced expressiveness.
  • Designed a multi-task learning decoder for reciprocal imputation of time series and graph structures.

Main Results:

  • PoGeVon demonstrated superior performance compared to existing baseline methods.
  • The RWR-based node embedding showed higher expressive power than traditional message-passing GNNs.
  • The model effectively imputes missing values in both time series features and graph structures.

Conclusions:

  • PoGeVon offers an effective solution for imputation in challenging NTS data.
  • The proposed method successfully integrates dynamic graph information for improved imputation accuracy.
  • This work advances the field of time series imputation by addressing complex networked data.