Carnegie Mellon University
Browse
Generalization Error Bounds for Time Series.pdf (1.73 MB)

Generalization Error Bounds for Time Series

Download (1.73 MB)
thesis
posted on 2012-04-06, 00:00 authored by Daniel J. McDonald

In this thesis, I derive generalization error bounds — bounds on the expected inaccuracy of the predictions — for time series forecasting models. These bounds allow forecasters to select among competing models, and to declare that, with high probability, their chosen model will perform well — without making strong assumptions about the data generating process or appealing to asymptotic theory. Expanding upon results from statistical learning theory, I demonstrate how these techniques can help time series forecasters to choose models which behave well under uncertainty. I also show how to estimate the β-mixing coefficients for dependent data so that my results can be used empirically. I use the bound explicitly to evaluate different predictive models for the volatility of IBM stock and for a standard set of macroeconomic variables. Taken together my results show how to control the generalization error of time series models with fixed or growing memory.

History

Date

2012-04-06

Degree Type

  • Dissertation

Department

  • Statistics

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

Cosma Shalizi,Mark Schervish

Usage metrics

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC