New robust forecasting models for exchange rates prediction

Authors:
Babita Majhi;Minakhi Rout;Ritanjali Majhi;Ganapati Panda;Peter J. Fleming
Affiliations:
Dept. of Automatic Control and System Eng., University of Sheffield, UK and Dept. of CS & IT, Guru Ghasidas Vishwavidyalaya (Central University), Bilaspur, India;Dept. of CSE, ITER, Siksha O Anusandhan (Deemed to be) University, Bhubaneswar, India;School of Management, National Institute of Technology, Warangal, India;School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India;Dept. of Automatic Control and System Eng., University of Sheffield, UK
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 16
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Abstract

This paper introduces two robust forecasting models for efficient prediction of different exchange rates for future months ahead. These models employ Wilcoxon artificial neural network (WANN) and Wilcoxon functional link artificial neural network (WFLANN). The learning algorithms required to train the weights of these models are derived by minimizing a robust norm called Wilcoxon norm. These models offer robust exchange rate predictions in the sense that the training of weight parameters of these models are not influenced by outliers present in the training samples. The Wilcoxon norm considers the rank or position of an error value rather than its amplitude. Simulation based experiments have been conducted using real life data and the results indicate that both models, unlike conventional models, demonstrate consistently superior prediction performance under different densities of outliers present in the training samples. Further, comparison of performance between the two proposed models reveals that both provide almost identical performance but the later involved low computational complexity and hence is preferable over the WANN model.