Generalization error bounds for stationary autoregressive models
Machine Learning
2011-06-06 v2 Machine Learning
Abstract
We derive generalization error bounds for stationary univariate autoregressive (AR) models. We show that imposing stationarity is enough to control the Gaussian complexity without further regularization. This lets us use structural risk minimization for model selection. We demonstrate our methods by predicting interest rate movements.
Cite
@article{arxiv.1103.0942,
title = {Generalization error bounds for stationary autoregressive models},
author = {Daniel J. McDonald and Cosma Rohilla Shalizi and Mark Schervish},
journal= {arXiv preprint arXiv:1103.0942},
year = {2011}
}
Comments
10 pages, 3 figures. CMU Statistics Technical Report