Abstract
Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.
Highlights
Since ancient times, gold has been recognized as a symbol of wealth and a frontierless currency that can be exchanged among different monetary systems [1, 2]
Algorithm 1 computed by wavelet neural network (WNN), and S denotes the number of training samples
Four sensitive-about-time macroeconomic indexes are taken as principal reflections of the short-term gold price in the future, namely, Dow Jones Industrial Average Index (DJIA), Consumer Price Index (CPI), US dollar index (USDX), and crude oil price (COP)
Summary
Gold has been recognized as a symbol of wealth and a frontierless currency that can be exchanged among different monetary systems [1, 2]. Ever since McCulloch and Pitts pioneering work [7], artificial models, such as back-propagation neural network (BP-NN) [8], radial basis function neural network (RBF-NN) [9], wavelet neural network (WNN) [10], Kohonen neural network [11], and Hopfield neural network [12] have been proposed and investigated. Internal-feedback ABC (IF-ABC) is applied for WNN parameter optimization when training a gold price prediction model. In this new algorithm, invalid trial time is taken as an index to reflect the internal status and to manipulate the exploration/exploitation intensity.
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