(Enter summary)
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... (Update)
Context of citations to this paper: More
.... or simply infeasible to analyze e.g. a large macroeconometric model or a neural net model, as in Rhee(1994) or LeBaron and Weigend(1994). 2 However, the postsample inferential procedures in the existing literature e.g. Ashley, Granger and Schmalensee(1980)...
.... to estimate the entire CTD with connectionist methods (e.g. Mixture Density Networks, Bishop, 1994; fractional binning, Srivastava Weigend, 1994) or with non connectionist methods such as a Monte Carlo on a hidden Markov model (Fraser Dimitriadis, 1994) While non...
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BibTeX entry: (Update)
LeBaron, A. & Weigend, A. (1994), Evaluating neural network predictors by bootstrapping, in `Proceedings of the International Conference on Neural Information Processing (ICONIP'94)', Seoul, Korea. http://citeseer.ist.psu.edu/article/lebaron94evaluating.html More
@misc{ lebaron94evaluating,
author = "A. LeBaron and A. Weigend",
title = "Evaluating neural network predictors by bootstrapping",
text = "LeBaron, A. & Weigend, A. (1994), Evaluating neural network predictors
by bootstrapping, in `Proceedings of the International Conference on Neural
Information Processing (ICONIP'94)', Seoul, Korea.",
year = "1994",
url = "citeseer.ist.psu.edu/article/lebaron94evaluating.html" }
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