CVTN: Cross Variable and Temporal Integration for Time Series Forecasting

arXiv:2404.18730v1 Announce Type: new
Abstract: In multivariate time series forecasting, the Transformer architecture encounters two significant challenges: effectively mining features from historical sequences and avoiding overfitting during the learning of temporal dependencies. To tackle these challenges, this paper deconstructs time series forecasting into the learning of historical sequences and prediction sequences, introducing the Cross-Variable and Time Network (CVTN). This unique method divides multivariate time series forecasting into two phases: cross-variable learning for effectively mining fea tures from historical sequences, and …

abstract architecture arxiv challenges cs.ai cs.lg dependencies features forecasting integration mining multivariate overfitting paper prediction series stat.ap temporal time series time series forecasting transformer transformer architecture type