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A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
HRST-LR: a hessian regularization spatio-temporal low rank algorithm for traffic data imputation
Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …
Pristi: A conditional diffusion framework for spatiotemporal imputation
Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow
modeling, and climate forecasting. However, the originally collected spatiotemporal data in …
modeling, and climate forecasting. However, the originally collected spatiotemporal data in …
Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent
transportation systems. Recent research has employed graph neural networks (GNNs) for …
transportation systems. Recent research has employed graph neural networks (GNNs) for …
An observed value consistent diffusion model for imputing missing values in multivariate time series
Missing values, which are common in multivariate time series, is most important obstacle
towards the utilization and interpretation of those data. Great efforts have been employed on …
towards the utilization and interpretation of those data. Great efforts have been employed on …