A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.
Multilayer Feedforward Networks are Universal Approximators.Neural Networks, 2(5):359–366, 1989
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Neural networks outperform traditional econometric models in yield curve forecasting accuracy and simulated bond trading performance for US and European markets.
citing papers explorer
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Bounding Global and Local Compression Error of Signal Parameterizations
A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.
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Data-Driven Duration Management -- Term Structure Forecasting Using Machine Learning
Neural networks outperform traditional econometric models in yield curve forecasting accuracy and simulated bond trading performance for US and European markets.