mlsanityData to 14 Oct 2026

SDA-GRIN for Adaptive Spatial-Temporal Multivariate Time Series Imputation

arXiv 2410.03954 ↗First posted 4 Oct 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

In various applications, the multivariate time series often suffers from missing data. This issue can significantly disrupt systems that rely on the data. Spatial and temporal dependencies can be leveraged to impute the missing samples. Existing imputation methods often ignore dynamic changes in spatial dependencies. We propose a Spatial Dynamic Aware Graph Recurrent Imputation Network (SDA-GRIN) which is capable of capturing dynamic changes in spatial dependencies.SDA-GRIN leverages a multi-head attention mechanism to adapt graph structures with time. SDA-GRIN models multivariate time series as a sequence of temporal graphs and uses a recurrent message-passing architecture for imputation. We evaluate SDA-GRIN on four real-world datasets: SDA-GRIN improves MSE by 9.51% for the AQI and 9.40% for AQI-36. On the PEMS-BAY dataset, it achieves a 1.94% improvement in MSE. Detailed ablation study demonstrates the effect of window sizes and missing data on the performance of the method. Project page:https://ameskandari.github.io/sda-grin/

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References · 15

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  11. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 95
  12. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
3 more
  1. 2023Dynamic Frequency Domain Graph Convolutional Network for Traffic Forecastingcited by 10
  2. 2024Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecastingcited by 3
  3. 2022State space model multiple imputation for missing data in non-stationary multivariate time series with application in digital Psychiatrycited by 1

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