Search Results for author:
Found 17 papers, 11 papers with code
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations
Virtual sensing techniques allow for inferring signals at new unmonitored locations by exploiting spatio-temporal measurements coming from physical sensors at different locations.
Graph Deep Learning for Time Series Forecasting
The conditioning can take the form of an architectural inductive bias on the neural forecasting architecture, resulting in a family of deep learning models called spatiotemporal graph neural networks.
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.
Graph Kalman Filters
The well-known Kalman filters model dynamical systems by relying on state-space representations with the next state updated, and its uncertainty controlled, by fresh information associated with newly observed system outputs.
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings.
Where and How to Improve Graph-based Spatio-temporal Predictors
The proposed AZ-analysis constitutes a valuable asset for discovering and highlighting those space-time regions where the model can be improved with respect to performance.
Graph state-space models
State-space models constitute an effective modeling tool to describe multivariate time series and operate by maintaining an updated representation of the system state from which predictions are made.
Sparse Graph Learning from Spatiotemporal Time Series
Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures.
AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs
We present the first whiteness test for graphs, i. e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph.
Understanding Pooling in Graph Neural Networks
Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs.
Graph Edit Networks
With this result, we hope to provide a firm theoretical basis for a next generation of time series prediction models.
Graph Random Neural Features for Distance-Preserving Graph Representations
We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks.
Autoregressive Models for Sequences of Graphs
This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models.
Change Point Methods on a Sequence of Graphs
Given a finite sequence of graphs, e. g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process generating the graphs.
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs.
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings
The proposed methodology consists in embedding graphs into a geometric space and perform change detection there by means of conventional methods for numerical streams.
Concept Drift and Anomaly Detection in Graph Streams
Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains.
