A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting

Abstract

Gold, as a dominant ingredient in financial market, has gripped a large quantities of the financiers and scholars to research the formation mechanism of its price. Academic circles spring up plenty of methods to analyze and predict the gold price, such techniques are based on linear regression (MLR), support vector machine (SVM), artificial neural network (ANN), respectively. However, the existing methods cannot track the random and nonlinear features of the gold price well. The accurate and effective estimation models are acceptable for researching the temporal sequence, at the same time, it will be a powerful tool for governments and investors to formulate strategies.In this paper, a novel combination technique is put forward based on independent component analysis (ICA) and gate recurrent unit neural network (GRUNN) methods, which called ICA-GRUNN. In the first place, due to the ICA is multichannel mixed-signal analysis technique, variational mode decomposition (VMD) technique is utilized to decompose the original temporal series into virtual multichannel mixed-signal. Next, statistically independent components (ICs) are separated out from the time sequence via ICA, and then, the influence factors of the gold price are analyzed from the aspect of ICs. The results demonstrate that the fluctuation of the gold price will be interrupted by long-term trends, cyclic recurrent factors and random events. Thirdly, applying GRUNN on ICs to acquire the prediction series of independent components (ICPs) and the forecasting result of the gold price is the combination of the ICPs. Finally, comparison experiments indicate that ICA-GRUNN provides prediction with high accuracy and outperforms the benchmark methods, autoregressive integrated moving average (ARIMA), radial basis function neural network (RBFNN), long short term memory neural network (LSTM), GRUNN and ICA-LSTM.