Aligning cause-effect pairs by their estimated Granger lag improves channel-dependent forecasting accuracy and transfer learning on synthetic time-series data.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Causal Time-Series Synchronization for Multi-Dimensional Forecasting
Aligning cause-effect pairs by their estimated Granger lag improves channel-dependent forecasting accuracy and transfer learning on synthetic time-series data.