L0- Feature selection method using autoregressive model and L0-group lasso and computing system performing the same

摘要

Disclosed is a method for selecting variables more rarely than an existing algorithm using a autoregressive model and a transform group Lasso to which L0-penalty is applied, and a variable selection system for performing the same. According to an aspect of the present invention, the variable selection system, acquiring time-series data of each of the m variables (variable), the variable selection system is based on the time-series data of each of the m variables, the N-th autoregressive model Generating m time series data clusters according to (Autoregressive Model), the variable selection system, the m time series data clusters by applying a transform group Lasso (L0- penalty) applied to the m time series data clusters A method of selecting a variable is provided, comprising selecting at least a portion of the variables and determining a variable corresponding to each of the selected at least some time series data clusters as a main variable.