Soft computing prediction of economic growth based in science and technology factors

The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to forecast the gross domestic product (GDP) growth rate. In this study the GDP growth was analyzed based on ten science and technology factors. These factors were: research and development (R&D) expenditure in GDP, scientific and technical journal articles, patent applications for nonresidents, patent applications for residents, trademark applications for nonresidents, trademark applications for residents, total trademark applications, researchers in R&D, technicians in R&D and high-technology exports. The ELM results were compared with genetic programming (GP), artificial neural network (ANN) and fuzzy logic results. Based upon simulation results, it is demonstrated that ELM has better forecasting capability for the GDP growth rate.

[1]  Amaury Lendasse,et al.  Extreme learning machine towards dynamic model hypothesis in fish ethology research , 2014, Neurocomputing.

[2]  António Rua,et al.  Forecasting Portuguese GDP with factor models: Pre- and post-crisis evidence , 2015 .

[3]  Chee Kheong Siew,et al.  Real-time learning capability of neural networks , 2006, IEEE Trans. Neural Networks.

[4]  Xiaowen Jin,et al.  Forecasting and Nowcasting Real GDP: Comparing Statistical Models and Subjective Forecasts , 2012 .

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

[6]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[7]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[8]  Lihua Feng,et al.  Application of artificial neural networks in tendency forecasting of economic growth , 2014 .

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

[10]  Libor Krkoska,et al.  How reliable are forecasts of GDP growth and inflation for countries with limited coverage , 2009 .

[11]  Theodore Modis,et al.  Long-Term GDP Forecasts and the Prospects for Growth , 2013 .

[12]  Dianhui Wang,et al.  Protein sequence classification using extreme learning machine , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

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

[14]  Sandra Stankiewicz,et al.  Forecasting GDP growth using mixed-frequency models with switching regimes , 2015 .

[15]  George Kapetanios,et al.  Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods , 2016, Comput. Stat. Data Anal..

[16]  Libor Krkoska,et al.  Accuracy of GDP growth forecasts for transition countries: Ten years of forecasting assessed , 2007 .

[17]  Joseph Zeira,et al.  Economic growth and sector dynamics , 2015 .

[18]  Pasquale Scaramozzino,et al.  Production complexity, adaptability and economic growth , 2016 .