DAG proposes a dual correlation network for time series forecasting with exogenous variables that captures temporal and channel correlations to better leverage future covariates.
Rethinking irregular time series forecasting: A simple yet effective baseline
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
Hermes is a multi-scale spatial-temporal hypergraph network that improves stock forecasting accuracy by capturing inter-industry lead-lag dependencies and fusing information across scales.
citing papers explorer
-
DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables
DAG proposes a dual correlation network for time series forecasting with exogenous variables that captures temporal and channel correlations to better leverage future covariates.
-
Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting
Hermes is a multi-scale spatial-temporal hypergraph network that improves stock forecasting accuracy by capturing inter-industry lead-lag dependencies and fusing information across scales.