Affiliations: [a] G.M.R. Institute of Technology, Rajam, Andhra Pradesh, India | [b] Siksha `O' Anusandhan University, Bhubaneswar, Odisha, India
Correspondence:
[*]
Corresponding author: P.K. Dash, G.M.R. Institute of Technology, Rajam, Andhra Pradesh, India. Tel.: +91 674 2727336; E-mail:[email protected]
Abstract: This paper proposes a novel nonlinear ensemble
forecasting model integrating functional link (FL) with radial basis
function (RBF) neural network in order to improve prediction performance. In
addition to the traditional parameters like the centers, widths and output
weights, the input weights of the connections between the input and hidden
layer are also adjusted during the training process. The developed algorithm
is introduced for designing a compact FLRBF (Functional link Radial Basis
Function) network and performing efficient training process. A certain set
of prominent trading indicators, together with the moving average
convergence/divergence and relative strength index, are also utilized in the
anticipated model. The proposed approach is applied to currency exchange
prediction to test the main properties of FLRBF network, including its
generalization ability, tolerance to input noise, and online learning
ability. More specifically, the trading and statistical performance of all
models are investigated in a forecast simulation of the exchange rates
between American Dollar and four other major currencies, Euro, Indian
Rupee, Canadian Dollar, Australian Dollar, etc. over the period January 2004
to January 2014 using the last years for out-of-sample testing. Further the
FLRBF network prediction performance is also compared with linear, nonlinear
and hybrid neural networks. As it turns out, the FLRBF architecture
outperforms all other models in terms of statistical accuracy and trading
efficiency for the three exchange rates. Different performance indicators
such as MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), and MAPE
(Mean Absolute Percentage Error) are employed in order to evaluate the
performance of the proposed model.
Keywords: Exchange rate forecasting, nonlinear modeling, hybrid model, Levenberg-Marquardt algorithm, radial basis function, improved second order algorithm
