### "Generalization Error Bounds for Time Series"

On Friday, my student Daniel
McDonald, who I have been lucky enough to jointly advise
with Mark
Schervish, defeated
the snake — that
is, defended his thesis:

- Generalization Error Bounds for Time Series
- 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 beta-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.
- PDF [2 Mb]

I hope to have a follow-up post very soon about the substance of Daniel's
work, which is part of our INET grant, but in the
meanwhile: congratulations, Dr. McDonald!

Kith and Kin;
Enigmas of Chance;
The Dismal Science

Posted at April 08, 2012 17:25 | permanent link