Blake LeBaron () (University of Wisconsin - Madison)
Abstract
We present a new method, inspired by the bootstrap, whose goal it is to determine the quality and reliability of a neural network predictor. Our method leads to more robust forecasting along with a large amount of statistical information on forecast performance that we exploit. We exhibit the method in the context of multi-variate time series prediction on financial data from the New York Stock Exchange. It turns out that the variation due to different resamplings (i.e., splits between training, cross-validation, and test sets) is significantly larger than the variation due to different network conditions (such as architecture, initial weights, etc.). Furthermore, this method allows us to forecast a probability distribution, as opposed to the traditional case of just a single value at each time step. We demonstrate this on a strictly held-out test set that includes the 1987 stock market crash. Finally, we compare the performance of the class of neural networks to a similarly bootstrapped linear model.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by University of Wisconsin - Madison in its series Working papers with number
_003.
Length: Date of creation: Date of revision: Handle: RePEc:wop:wimahp:_003
Contact details of provider: Postal: Social Science Building, 1180 Observatory Drive, Madison, WI 53706-1393 Phone: 608/263-2989 Fax: 608/262-2033 Email: Web page: http://www.econ.wisc.edu/ More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (Thomas Krichel).