Prediction of White Noise Time Series using Artificial Neural Networks and Asymmetric Cost Functions

Abstract

Abstract- Artificial neural networks in time series prediction generally minimise a symmetric statistical error, such as the sum of squared errors, to learn relationships from the presented data. However, applications in business elucidate that real forecastine-. rrroblems contain non-svmmetric errors. The costs arising from suboptimal business decisions based on overversus underprediction are dissimilar for errors of identical magnitude. To reflect this, a set of asymmetric cost functions is used as objective functions for neural network training, deriving suoerior forecasts even for white noise time series, some Artificial neural networks (ANN) have found 'increasing consideration in forecasting theory, leading to successful applications in time series and explanatory sales forecasting [5,19,22]. In management, forecasts are a prerequisite for all decisions based upon planning [Z]. Therefore, the quality of