Graph state-space models

01/04/2023
by   Daniele Zambon, et al.
1

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. Within this framework, relational inductive biases, e.g., associated with functional dependencies existing among signals, are not explicitly exploited leaving unattended great opportunities for effective modeling approaches. The manuscript aims, for the first time, at filling this gap by matching state-space modeling and spatio-temporal data where the relational information, say the functional graph capturing latent dependencies, is learned directly from data and is allowed to change over time. Within a probabilistic formulation that accounts for the uncertainty in the data-generating process, an encoder-decoder architecture is proposed to learn the state-space model end-to-end on a downstream task. The proposed methodological framework generalizes several state-of-the-art methods and demonstrates to be effective in extracting meaningful relational information while achieving optimal forecasting performance in controlled environments.

READ FULL TEXT
research
05/26/2022

Sparse Graph Learning for Spatiotemporal Time Series

Outstanding achievements of graph neural networks for spatiotemporal tim...
research
07/31/2021

Multivariate Time Series Imputation by Graph Neural Networks

Dealing with missing values and incomplete time series is a labor-intens...
research
05/30/2023

Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting

Existing relationships among time series can be exploited as inductive b...
research
06/10/2021

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

Spatio-temporal forecasting has numerous applications in analyzing wirel...
research
01/13/2020

Relational State-Space Model for Stochastic Multi-Object Systems

Real-world dynamical systems often consist of multiple stochastic subsys...
research
05/05/2023

Analyzing Ecological Momentary Assessment Data with State-Space Models: Considerations and Recommendations

Ecological momentary assessment (EMA) data have a broad base of applicat...
research
03/21/2023

Graph Kalman Filters

The well-known Kalman filters model dynamical systems by relying on stat...

Please sign up or login with your details

Forgot password? Click here to reset