Training artificial neural 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 from 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 asymmetric cost functions is proposed as novel error functions for neural network training. Consequently, a neural network minimizes an asymmetric cost function to derive forecasts considered preeminent regarding the original problem. Some experimental results in forecasting a stationary time series using a multilayer perceptron trained with a linear asymmetric cost function are computed, evaluating the performance in competition to basic forecast methods using various error measures.