AI RESEARCH
Enhanced Random Subspace Local Projections for High-Dimensional Time Series Analysis
arXiv CS.LG
•
ArXi:2603.07500v1 Announce Type: new High-dimensional time series forecasting suffers from severe overfitting when the number of predictors exceeds available observations, making standard local projection methods unstable and unreliable. We propose an enhanced Random Subspace Local Projection (RSLP) framework designed to deliver robust impulse response estimation in the presence of hundreds of correlated predictors. The method