# On Selecting Stable Predictors in Time Series Models

## TL;DR

This paper introduces a new predictor selection method for time series that accounts for data dependence, improving over traditional methods like lasso, with demonstrated benefits on simulated and real datasets.

## Contribution

It extends feature selection to dependent data using mixing processes, providing finite-sample guarantees and practical applicability for time series analysis.

## Key findings

- Improved predictor stability in dependent data settings
- Finite-sample performance guarantees
- Effective on both simulated and real datasets

## Abstract

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.