Fast, effective, and coherent time series modeling using the sparsity-ranked lasso
使用稀疏性排序 Lasso 进行快速、有效且一致的时间序列建模
The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet of the SRL is that an algorithm should be more skeptical of higher-order polynomials and interactions *a priori* compared to main effects, and hence the inclusion of these more complex terms should require a higher level of evidence. In time series, the same idea of ranked prior skepticism can be applied to the possibly…