Gold price forecasting research based on an improved online extreme learning machine algorithm

Accurate gold price prediction is highly essential for economic and currency markets. Thus, the intelligence prediction models need to be applied to price prediction. On the basis of long-term collected daily gold, the study proposes a novel genetic algorithm regularization online extreme learning machine (GA-ROSELM), to predict gold price data which collected from public websites. Akaike Information Criterion (AIC) is introduced to build the eight input combinations of variables based on the silver price of the previous day (Silver_D1), Standard & Poor. The 500 indexes (S&P_D1), the crude oil price (Crude_D1), and the gold price of the previous 3 days (Gold_D1, Gold_D2, Gold_D3). Eight optimal variable models are established, and the final input variables are determined according to the minimum AIC value. The proposed model (GA-ROSELM) solve the problem that OS-ELM model which is easy to generate singular matrices, meanwhile, experiments demonstrate this model performs better than ARIMA, SVM, BP, ELM and OS-ELM in the gold price prediction experiment. On the test set, the root means square error of this model (GA-ROSELM) prediction compared with five other models which decreased by 13.1%, 22.4%, 53.87%, 57.84% and 37.72% respectively. In summary, the results clearly confirm the effectiveness of the GA-ROSELM model.

[1]  Ji Chen,et al.  Regularization incremental extreme learning machine with random reduced kernel for regression , 2018, Neurocomputing.

[2]  Patrick M. Stephan,et al.  The Gold Price in Times of Crisis , 2013 .

[3]  Alexandros Iosifidis,et al.  Graph Embedded Extreme Learning Machine , 2016, IEEE Transactions on Cybernetics.

[4]  S. Kumar Chandar,et al.  Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction , 2019, Journal of Ambient Intelligence and Humanized Computing.

[5]  J. Stock,et al.  A Probability Model of the Coincident Economic Indicators , 1988 .

[6]  C. Casanova-Mateo,et al.  Hybridizing Extreme Learning Machines and Genetic Algorithms to select acoustic features in vehicle classification applications , 2015, Neurocomputing.

[7]  L. Blose,et al.  Gold prices, cost of carry, and expected inflation , 2010 .

[8]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[9]  Liu Hongjuan,et al.  A new hybrid constructive neural network method for impacting and its application on tungsten price prediction , 2017, Applied Intelligence.

[10]  Fernando Pérez-Cruz,et al.  Enhancing genetic feature selection through restricted search and Walsh analysis , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Jun Wang,et al.  Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network , 2018 .

[12]  Yongming Huang,et al.  Feature fusion methods research based on deep belief networks for speech emotion recognition under noise condition , 2017, Journal of Ambient Intelligence and Humanized Computing.

[13]  Nammee Moon,et al.  BiLSTM model based on multivariate time series data in multiple field for forecasting trading area , 2019, Journal of Ambient Intelligence and Humanized Computing.

[14]  G. Grudnitski,et al.  Forecasting S&P and gold futures prices: An application of neural networks , 1993 .

[15]  Yunsong Li,et al.  Hyperspectral image reconstruction by deep convolutional neural network for classification , 2017, Pattern Recognit..

[16]  Florentina Paraschiv,et al.  Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks , 2016 .

[17]  Guang Chen,et al.  Gold prices fluctuation of co-movement forecast between China and Russia , 2019, Resources Policy.

[18]  Yonggwan Won,et al.  Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks , 2011, Pattern Recognit. Lett..

[19]  Cheng Wu,et al.  Domain Space Transfer Extreme Learning Machine for Domain Adaptation , 2019, IEEE Transactions on Cybernetics.

[20]  Werner Kristjanpoller,et al.  Gold price volatility: A forecasting approach using the Artificial Neural Network-GARCH model , 2015, Expert Syst. Appl..

[21]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Erkan Topal,et al.  An overview of global gold market and gold price forecasting , 2010 .

[23]  Rob J. Hyndman,et al.  Crude oil price forecasting based on internet concern using an extreme learning machine , 2018, International Journal of Forecasting.

[24]  KristjanpollerWerner,et al.  Gold price volatility , 2015 .

[25]  Xiu Li,et al.  Deep learning with stock indicators and two-dimensional principal component analysis for closing price prediction system , 2016, 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS).

[26]  Haibo Zhou,et al.  Research on agricultural product price forecasting model based on improved BP neural network , 2018, Journal of Ambient Intelligence and Humanized Computing.

[27]  Emmanuel Paroissien,et al.  Forecasting bulk prices of Bordeaux wines using leading indicators , 2020 .

[28]  P.B. Luh,et al.  Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method , 2005, IEEE Transactions on Power Systems.

[29]  Fengyi Zhang,et al.  Gold Price Forecasting Based on RBF Neural Network and Hybrid Fuzzy Clustering Algorithm , 2014 .

[30]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[31]  Mohd Herwan Sulaiman,et al.  Training LSSVM with GWO for price forecasting , 2015, 2015 International Conference on Informatics, Electronics & Vision (ICIEV).

[32]  Feng Pan,et al.  Online sequential extreme learning machine based multilayer perception with output self feedback for time series prediction , 2013 .

[33]  Kumar Chandar Sivalingam,et al.  Forecasting Gold Prices Based on Extreme Learning Machine , 2016, Int. J. Comput. Commun. Control.

[34]  Zaher Mundher Yaseen,et al.  Predicting compressive strength of lightweight foamed concrete using extreme learning machine model , 2018, Adv. Eng. Softw..

[35]  Hong-yu Zhang,et al.  A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting , 2019, Technological Forecasting and Social Change.

[36]  Dirk G. Baur,et al.  A melting pot — Gold price forecasts under model and parameter uncertainty , 2016 .

[37]  Jian Chai,et al.  A new information fusion method of forecasting , 2019, J. Ambient Intell. Humaniz. Comput..

[38]  H. Akaike.  A new look at the statistical model identification , 1974 .

[39]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.