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Networked time series imputation via position-aware graph enhanced variational autoencoders
Wang, Dingsu
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https://hdl.handle.net/2142/120367
Description
- Title
- Networked time series imputation via position-aware graph enhanced variational autoencoders
- Author(s)
- Wang, Dingsu
- Issue Date
- 2023-04-14
- Director of Research (if dissertation) or Advisor (if thesis)
- Tong, Hanghang
- Department of Study
- Computer Science
- Discipline
- Computer Science
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- M.S.
- Degree Level
- Thesis
- Keyword(s)
- Networked time series
- imputation
- variational autoencoders
- random walk with restart
- node positional embeddings
- Abstract
- Multivariate time series (MTS) imputation is a widely studied problem in recent years. Existing methods can be divided into two main groups, including (1) deep recurrent or generative models that primarily focus on time series features, and (2) graph neural networks (GNNs) based models that utilize the topological information from the inherent graph structure of MTS as relational inductive bias for imputation. Nevertheless, these methods either neglect topological information or assume the graph structure is fixed and accurately known. Thus, they fail to fully utilize the graph dynamics for precise imputation in more challenging MTS data such as networked time series (NTS), where the underlying graph is constantly changing and might have missing edges. In this thesis, we propose a novel approach to overcome these limitations. First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures. Then, we design a new model named PoGeVon which leverages variational autoencoder (VAE) to predict missing values over both node time series features and graph structures. In particular, we propose a new node position embedding based on random walk with restart (RWR) in the encoder with provable higher expressive power compared with message-passing based graph neural networks (GNNs). We further design a decoder with 3-stage predictions from the perspective of multi-task learning to impute missing values in both time series and graph structures reciprocally. Experiment results demonstrate the effectiveness of our model over baselines.
- Graduation Semester
- 2023-05
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/120367
- Copyright and License Information
- Copyright 2023 Dingsu Wang
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