  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.
