@article{TEXTUAL,
      recid = {13012},
      author = {Huang, Shuo-Chieh and Tsay, Ruey S.},
      title = {Time Series Forecasting with Many Predictors},
      journal = {Mathematics},
      address = {2024-07-26},
      number = {TEXTUAL},
      abstract = {We propose a novel approach for time series forecasting  with many predictors, referred to as the GO-sdPCA, in this  paper. The approach employs a variable selection method  known as the group orthogonal greedy algorithm and the  high-dimensional Akaike information criterion to mitigate  the impact of irrelevant predictors. Moreover, a novel  technique, called peeling, is used to boost the variable  selection procedure so that many factor-relevant predictors  can be included in prediction. Finally, the supervised  dynamic principal component analysis (sdPCA) method is  adopted to account for the dynamic information in factor  recovery. In simulation studies, we found that the proposed  method adapts well to unknown degrees of sparsity and  factor strength, which results in good performance, even  when the number of relevant predictors is large compared to  the sample size. Applying to economic and environmental  studies, the proposed method consistently performs well  compared to some commonly used benchmarks in one-step-ahead  out-sample forecasts.},
      url = {http://knowledge.uchicago.edu/record/13012},
}