On Selecting Stable Predictors in Time Series Models
This work addresses feature selection for time series modeling, which is incremental as it builds on existing methods like lasso by incorporating dependence structures.
The authors tackled the problem of feature selection in time series data by proposing a novel predictor selection scheme that accounts for statistical dependence, extending i.i.d. sub-sampling frameworks. They demonstrated improvements over base methods like lasso on simulated and real datasets, quantifying gains in finite samples.
We extend the feature selection methodology to dependent data and propose a novel time series predictor selection scheme that accommodates statistical dependence in a more typical i.i.d sub-sampling based framework. Furthermore, the machinery of mixing stationary processes allows us to quantify the improvements of our approach over any base predictor selection method (such as lasso) even in a finite sample setting. Using the lasso as a base procedure we demonstrate the applicability of our methods to simulated and several real time series datasets.