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) …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Spatial-temporal aware inductive graph neural network for C-ITS data recovery
With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …
A review of location encoding for GeoAI: methods and applications
ABSTRACT A common need for artificial intelligence models in the broader geoscience is to
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …
Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data
In real-world intelligent transportation systems, the spatiotemporal traffic data collected from
sensors often exhibit missing or corrupted data, significantly hindering the development of …
sensors often exhibit missing or corrupted data, significantly hindering the development of …
Filling the g_ap_s: Multivariate time series imputation by graph neural networks
Dealing with missing values and incomplete time series is a labor-intensive, tedious,
inevitable task when handling data coming from real-world applications. Effective spatio …
inevitable task when handling data coming from real-world applications. Effective spatio …
ImputeFormer: Low rankness-induced transformers for generalizable spatiotemporal imputation
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the
modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank …
modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank …
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 …
Ginar: An end-to-end multivariate time series forecasting model suitable for variable missing
Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely
forecast the future values/trends, based on the complex relationships identified from …
forecast the future values/trends, based on the complex relationships identified from …