Training Artificial Nexjral Networks for Time Series Prediction Using Asymmetric Cost Functions

Abstract

Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships fiom the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in magnitude, cause different costs. To reflect this, a set of… (More)

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