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A convolution recurrent autoencoder for spatio-temporal missing data
  imputation

A convolution recurrent autoencoder for spatio-temporal missing data imputation

29 April 2019
Reza Asadi
Amelia Regan
    SyDa
ArXivPDFHTML

Papers citing "A convolution recurrent autoencoder for spatio-temporal missing data imputation"

4 / 4 papers shown
Title
Large-Scale Traffic Data Imputation with Spatiotemporal Semantic
  Understanding
Large-Scale Traffic Data Imputation with Spatiotemporal Semantic Understanding
Kunpeng Zhang
Lan Wu
Liang Zheng
Na Xie
Zhengbing He
AI4TS
23
1
0
27 Jan 2023
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic
  Data Recovery
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery
Yuting Ding
Dingjie Wu
11
0
0
16 Aug 2022
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic
  Data Imputation with Complex Missing Patterns
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
Yuebing Liang
Zhan Zhao
Lijun Sun
GNN
AI4TS
29
61
0
17 Sep 2021
BlackBox: Generalizable Reconstruction of Extremal Values from
  Incomplete Spatio-Temporal Data
BlackBox: Generalizable Reconstruction of Extremal Values from Incomplete Spatio-Temporal Data
T. Ivek
Domagoj Vlah
43
4
0
30 Apr 2020
1