Title :
Forecasting currency exchange rates with an Artificial Bee Colony-optimized neural network
Author :
Worasucheep, Chukiat
Author_Institution :
Applied Computer Science, Department of Mathematics, Faculty of Science, King Mongkut´s University of Technology Thonburi, Thailand
Abstract :
This paper applies a recent variant of Artificial Bee Colony (ABC) to optimize the weights of a three-layer feedforward neural network for forecasting of currency exchange rates of USD/EUR and USD/Yen. The inputs to the network is built from historical prices and a set of well-known technical indicators, including Moving Average, Moving Average Convergence/Divergence and Relative Strength Index. The forecasting model becomes a complex minimization problem with fifty-decision variables, many of which are interdependent. The ABC variant in this work is ABCDE that is a hybrid algorithm of original ABC with two different mutation strategies of Differential Evolution (DE). The experimental results present a superior performance of ABCDE in terms of both training and testing errors against original ABC, Back Propagation and ODE [35], an efficient variant of DE.
Keywords :
Artificial neural networks; Exchange rates; Forecasting; Testing; Time series analysis; Training; Artificial Bee Colony; Forecasting; Foreign exchange rate; Neural Network;
Conference_Titel :
Evolutionary Computation (CEC), 2015 IEEE Congress on
Conference_Location :
Sendai, Japan
DOI :
10.1109/CEC.2015.7257305