Generalization error bounds for stationary autoregressive models
  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. 
Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish (Carnegie   Mellon University)
stat.ML cs.LG