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Abstract: This article exposes problems of the commonly used technique of splitting the available data into training, validation, and test sets that are held fixed, warns about drawing too strong conclusions from such static splits, and shows potential pitfalls of ignoring variability across splits. Using a bootstrap or resampling method, we compare the uncertainty in the solution stemming from the data splitting with neural network specific uncertainties (parameter initialization, choice of number of... (Update)
Context of citations to this paper: More
...residuals. Another type considers each training case (y i ; x i ) as the sampling units. This is called bootstrap pairs. LeBaron and Weigend (1998) propose a method similar to the bootstrap pairs approach, and also consider randomness arising from both the splitting of the...
.... techniques to the financial context, researchers are working on ways to improve the forecastability of neural network based models [2, 3, 6]. Research shows that conventional statistical techniques for forecasting have reached their limitation in applications with...
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BibTeX entry: (Update)
LeBaron, B. and A. S. Weigend (1998): "A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series," IEEE Transactions on Neural Netwroks, 213-220. http://citeseer.ist.psu.edu/lebaron98bootstrap.html More
@article{ lebaron98bootstrap,
author = "B. LeBaron and A. S. Weigend",
title = "A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series",
journal = "IEEE Transactions on Neural Networks",
volume = "9",
number = "1",
month = "January",
pages = "213--220",
year = "1998",
url = "citeseer.ist.psu.edu/lebaron98bootstrap.html" }
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