Generalization error bounds for stationary autoregressive models

نویسندگان

  • Daniel J. McDonald
  • Cosma Rohilla Shalizi
  • Mark J. Schervish
چکیده

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.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Forecasting Non-Stationary Time Series: From Theory to Algorithms

Generalization bounds for time series prediction and other non-i.i.d. learning scenarios that can be found in the machine learning and statistics literature assume that observations come from a (strictly) stationary distribution. The first bounds for completely non-stationary setting were proved in [6]. In this work we present an extension of these results and derive novel algorithms for foreca...

متن کامل

Time series forecasting: model evaluation and selection using nonparametric risk bounds

We derive generalization error bounds — bounds on the expected inaccuracy of the predictions — for traditional time series forecasting models. Our results hold for many standard forecasting tools including autoregressive models, moving average models, and, more generally, linear state-space models. These bounds allow forecasters to select among competing models and to guarantee that with high p...

متن کامل

Nonparametric Risk Bounds for Time-Series Forecasting

We derive generalization error bounds for traditional time-series forecasting models. Our results hold for many standard forecasting tools including autoregressive models, moving average models, and, more generally, linear state-space models. These non-asymptotic bounds need only weak assumptions on the data-generating process, yet allow forecasters to select among competing models and to guara...

متن کامل

Generalized Autoregressive Conditional Heteroskedasticity

A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an e...

متن کامل

On Estimating Conditional Mean-squared Prediction Error in Autoregressive Models by Ching-kang Ing

Zhang and Shaman considered the problem of estimating the conditional mean-squared prediciton error (CMSPE) for a Gaussian autoregressive (AR) process. They used the final prediction error (FPE) of Akaike to estimate CMSPE and proposed that FPE’s effectiveness be judged by its asymptotic correlation with CMSPE. However, as pointed out by Kabaila and He, the derivation of this correlation by Zha...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • CoRR

دوره abs/1103.0942  شماره 

صفحات  -

تاریخ انتشار 2011