Search Results for author:
Found 8 papers, 1 papers with code
Non-collective Calibrating Strategy for Time Series Forecasting
Deep learning-based approaches have demonstrated significant advancements in time series forecasting.
Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning
In light of this, we introduce a novel task, Expanding-variate Time Series Forecasting (EVTSF).
Contrastive Learning
Multivariate Time Series Forecasting
+2
Causally-Aware Unsupervised Feature Selection Learning
Additionally, previous graph-based methods fail to account for the differing impacts of non-causal and causal features in constructing the similarity graph, which leads to false links in the generated graph.
UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method
Accurate prediction of metro Origin-Destination (OD) flow is essential for the development of intelligent transportation systems and effective urban traffic management.
Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting
Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT).
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS).
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction
Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix.
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting
In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations.
