Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

Graph-based deep learning models are powerful in modeling spatio-temporal graphs for
traffic forecasting. In practice, accurate forecasting models rely on sufficient traffic data …

This paper introduces a new approach for Multivariate Time Series forecasting that jointly
infers and leverages relations among time series. Its modularity allows it to be integrated …

In the Industrial Internet of Things (IIoT), condition monitoring sensor signals from complex
systems often exhibit nonlinear and stochastic spatial-temporal dynamics under varying …

Outstanding achievements of graph neural networks for spatiotemporal time series analysis
show that relational constraints introduce an effective inductive bias into neural forecasting …

Accurate forecasting of multivariate time series is an extensively studied subject in finance,
transportation, and computer science. Fully mining the correlation and causation between …

Many financial forecasting deep learning works focus on the single task of predicting stock
returns for trading with unimodal numerical inputs. Investment and risk management …

Accurate traffic flow forecasting is vital for intelligent transportation systems, especially with
urbanization worsening traffic congestion, which affects daily life, economic growth, and the …

Many deep learning works on financial time-series forecasting focus on predicting future
prices/returns of individual assets with numerical price-related information for trading, and …

Multi-variate time series forecasting is an important problem with a wide range of
applications. Recent works model the relations between time-series as graphs and have …