Application of Nonlinear Autoregressive with Exogenous Input (Narx) Neural Network in Macroeconomic Forecasting, National Goal Setting and Global Competitiveness Assessment

This paper selects the NARX neural network as the method through literature review, and constructs specific NARX neural networks under application scenarios involving macroeconomic forecasting, national goal setting and global competitiveness assessment. Through case studies on China, US and Eurozone, this study explores how those limited & partial exogenous inputs or abundant & comprehensive exogenous inputs, a small set of most relevant exogenous inputs or a large set of exogenous inputs covering all major aspects of the macro economy, whole area related exogenous inputs or both whole area and subdivision area related exogenous inputs specifically affect the forecasting performance of NARX neural networks for specific macroeconomic indicators or indices. Through the case study on Russia this paper explores how the limited & most relevant exogenous inputs set or the abundant & comprehensive exogenous inputs set specifically influences the prediction performance of those specific NARX neural networks for national goal setting. Finally, comparative studies on the application of NARX neural networks for the forecasts of Global Competitiveness Indices (GCIs) of various economies are conducted, in order to explore whether the specific NARX neural network trained on the basis of the GCI related data of some economies can make sufficiently accurate predictions about GCIs of other economies, and whether the specific NARX neural network trained on the basis of the data of some type of economies can give more accurate predictions about GCIs of the same type of economies than those of different type of economies. Based on all of the above successful application, this paper provides policy recommendations on applying fully trained NARX neural networks that are assessed as qualified to assist or even replace the deductive and inductive abilities of the human brain in a variety of appropriate tasks.

[1]  Ying Chen,et al.  Energy resources demand-supply system analysis and empirical research based on non-linear approach , 2011 .

[2]  Rui Liu,et al.  Effective long short-term memory with differential evolution algorithm for electricity price prediction , 2018, Energy.

[3]  Ling Tang,et al.  Ensemble Forecasting for Complex Time Series Using Sparse Representation and Neural Networks , 2017 .

[4]  M. A. Nematollahi,et al.  Energy intensity and its components in Iran: Determinants and trends , 2018, Energy Economics.

[5]  Ning Yun-cai.  A superposition wavelet-neural network model of coal demand forecast , 2003 .

[6]  Gautam Mitra,et al.  Applications of news analytics in finance: A review , 2012 .

[7]  Chang You Wu,et al.  Coal Demand Prediction in Shandong Province Based on Artificial Firefly Wavelet Neural Network , 2014 .

[8]  Hou Tie-sha.  Research of RMB Exchange Rate Forecasting Based on the NARX Model , 2015 .

[9]  Mei Sun,et al.  The impacts of carbon tax on energy intensity and economic growth – A dynamic evolution analysis on the case of China , 2013 .

[10]  Jianhua Zhang,et al.  A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids , 2014 .

[11]  Grzegorz Dudek,et al.  Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting , 2016 .

[12]  Konstantinos Liagkouras,et al.  Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review , 2012, Expert Syst. Appl..

[14]  Yong Hu,et al.  Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review , 2015, Appl. Soft Comput..

[15]  Tshilidzi Marwala,et al.  Missing data: A comparison of neural network and expectation maximization techniques , 2007 .

[16]  Chuanmin Hu,et al.  The development of a non-linear autoregressive model with exogenous input (NARX) to model climate-water clarity relationships: reconstructing a historical water clarity index for the coastal waters of the southeastern USA , 2017, Theoretical and Applied Climatology.

[17]  Dario Sansone.  Beyond Early Warning Indicators: High School Dropout and Machine Learning , 2017, Oxford Bulletin of Economics and Statistics.

[18]  Adnan Sözen,et al.  Future projection of the energy dependency of Turkey using artificial neural network , 2009 .

[19]  Ioannis P. Panapakidis,et al.  An integrated model for risk management in electricity trade , 2017 .

[20]  C. Aloui,et al.  Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling , 2012 .

[21]  Ali F. Alajmi,et al.  Quantitative assessment of energy conservation due to public awareness campaigns using neural networks , 2010 .

[22]  Adnan Sözen,et al.  Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies , 2007 .

[23]  S. Jomnonkwao,et al.  Projection of future transport energy demand of Thailand , 2011 .

[24]  Wei Yu,et al.  A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends , 2018, IEEE Access.

[25]  Yonghui Sun,et al.  A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks , 2016 .

[26]  Sandra M. Guzmán,et al.  The Use of NARX Neural Networks to Forecast Daily Groundwater Levels , 2017, Water Resources Management.

[27]  Ahmet Murat Ozbayoglu,et al.  Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 , 2019, Appl. Soft Comput..

[28]  Yishan Ding,et al.  A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting , 2018, Energy.

[29]  Qinghua Huang,et al.  Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches , 2014, Knowl. Based Syst..

[30]  Jolanta Szoplik,et al.  Forecasting of natural gas consumption with artificial neural networks , 2015 .

[31]  M. M. Ardehali,et al.  LONG-TERM ELECTRICAL ENERGY CONSUMPTION FORECASTING FOR DEVELOPING AND DEVELOPED ECONOMIES BASED ON DIFFERENT OPTIMIZED MODELS AND HISTORICAL DATA TYPES , 2014 .

[32]  K. Lai,et al.  Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm , 2008 .

[33]  Antanas Verikas,et al.  Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey , 2010, Soft Comput..

[34]  Colm Kearney,et al.  Textual Sentiment in Finance: A Survey of Methods and Models , 2013 .

[35]  Maximilian Kasy.  Optimal taxation and insurance using machine learning — Sufficient statistics and beyond , 2018, Journal of Public Economics.

[36]  Ali Safari,et al.  Oil price forecasting using a hybrid model , 2018 .

[37]  Fotios Pasiouras,et al.  Assessing Bank Efficiency and Performance with Operational Research and Artificial Intelligence Techniques: A Survey , 2009, Eur. J. Oper. Res..

[38]  Lixin Tian,et al.  The evolution model of electricity market on the stable development in China and its dynamic analysis , 2016 .

[39]  Eugen Hristev.  NARX neural networks for sequence processing tasks , 2012 .

[40]  Zhao Guo-hao.  Forecasting Model of Coal Demand Based on Matlab BP Neural Network , 2008 .

[41]  Alireza Talaei,et al.  Predicting oil price movements: A dynamic Artificial Neural Network approach , 2014 .

[42]  Jin Xiao,et al.  A hybrid model based on selective ensemble for energy consumption forecasting in China , 2018, Energy.

[43]  Marc-André Mittermayer,et al.  Text Mining Systems for Market Response to News: A Survey , 2007 .

[44]  Ying Wah Teh,et al.  Credit Scoring Models Using Soft Computing Methods: A Survey , 2010, Int. Arab J. Inf. Technol..

[45]  Bill McDonald,et al.  Textual Analysis in Accounting and Finance: A Survey , 2016 .

[46]  Yong Hu,et al.  The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..

[47]  Yannis Manolopoulos,et al.  Data Mining on Finance and Accounting: a Review of Current Research Trends , 2004 .

[48]  Feng Li.  Textual Analysis of Corporate Disclosures: A Survey of the Literature , 2011 .

[49]  Wei-Yang Lin,et al.  Machine Learning in Financial Crisis Prediction: A Survey , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[50]  Michael Luca,et al.  Supplemental Appendix for : Productivity and Selection of Human Capital with Machine Learning , 2016 .

[51]  Ioannis P. Panapakidis,et al.  Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model , 2017 .

[52]  Ertuğrul Çam,et al.  Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines , 2015 .

[53]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[54]  José Salvador Sánchez,et al.  A literature review on the application of evolutionary computing to credit scoring , 2013, J. Oper. Res. Soc..

[55]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[56]  D. Hemanth,et al.  Monitoring the Impact of Economic Crisis on Crime in India Using Machine Learning , 2019 .

[57]  Shiguo Wang,et al.  A Comprehensive Survey of Data Mining-Based Accounting-Fraud Detection Research , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[58]  Vadlamani Ravi,et al.  A survey of the applications of text mining in financial domain , 2016, Knowl. Based Syst..

[59]  Sílvio Mariano,et al.  A bat optimized neural network and wavelet transform approach for short-term price forecasting , 2018 .

[60]  Jure Leskovec,et al.  Human Decisions and Machine Predictions , 2017, The quarterly journal of economics.

[61]  Thomas Heckelei,et al.  Machine learning in agricultural and applied economics , 2019, European Review of Agricultural Economics.

[62]  Ali Azadeh,et al.  An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments , 2010 .

[63]  S. Sheridan,et al.  A new approach to modeling temperature‐related mortality: Non‐linear autoregressive models with exogenous input , 2018, Environmental research.

[64]  Hamed Ghoddusi,et al.  Machine learning in energy economics and finance: A review , 2019 .

[65]  Zong Woo Geem,et al.  Transport energy demand modeling of South Korea using artificial neural network , 2011 .

[66]  Susan Athey,et al.  Beyond prediction: Using big data for policy problems , 2017, Science.

[67]  Olivier Grunder,et al.  Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm , 2017 .

[68]  Ioannis P. Panapakidis,et al.  Day-ahead electricity price forecasting via the application of artificial neural network based models , 2016 .

[69]  Wai Ming To,et al.  Modeling of electricity consumption in the Asian gaming and tourism center—Macao SAR, People's Republic of China , 2008 .

[70]  Ning Chen,et al.  Financial credit risk assessment: a recent review , 2015, Artificial Intelligence Review.

[71]  Steven C. H. Hoi,et al.  Online portfolio selection: A survey , 2012, CSUR.

[72]  Shanling Li,et al.  The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm , 2018 .

[73]  J. Contreras,et al.  Forecasting electricity prices for a day-ahead pool-based electric energy market , 2005 .

[74]  Jun Wang,et al.  Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations , 2016 .

[75]  Ashwani Kumar,et al.  Electricity price forecasting in deregulated markets: A review and evaluation , 2009 .

[76]  Monjur Mourshed,et al.  Forecasting methods in energy planning models , 2018 .

[77]  Kate Smith-Miles,et al.  A Comprehensive Survey of Data Mining-based Fraud Detection Research , 2010, ArXiv.

[78]  Hamid Asgari,et al.  Modelling, Simulation and Control of Gas Turbines Using Artificial Neural Networks , 2014 .

[79]  S. Moshiri,et al.  Forecasting Nonlinear Crude Oil Futures Prices , 2006 .

[80]  Vadlamani Ravi,et al.  Soft computing system for bank performance prediction , 2008, Appl. Soft Comput..

[81]  Maria Mrówczyńska,et al.  Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: The town of Zielona Gora , 2017 .

[82]  Susan Athey,et al.  The State of Applied Econometrics - Causality and Policy Evaluation , 2016, 1607.00699.

[83]  Roger Ghanem,et al.  Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks , 2017 .

[84]  Ahmet Murat Ozbayoglu,et al.  Deep Learning for Financial Applications : A Survey , 2020, Appl. Soft Comput..

[85]  Adnan Sözen,et al.  Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey , 2007 .

[86]  Andrea Prat,et al.  CEO Behavior and Firm Performance , 2017, Journal of Political Economy.

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

[88]  Yetis Sazi Murat,et al.  Use of artificial neural networks for transport energy demand modeling , 2006 .

[89]  B. Moreno,et al.  A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors , 2016 .

[90]  J. Kleinberg,et al.  Prediction Policy Problems. , 2015, The American economic review.

[91]  Nitin Singh,et al.  Short term electricity price forecast based on environmentally adapted generalized neuron , 2017 .

[92]  Lixin Tian,et al.  Chaotic characteristic identification for carbon price and an multi-layer perceptron network prediction model , 2015, Expert Syst. Appl..

[93]  Ibrahim Dincer,et al.  Artificial neural network analysis of world green energy use , 2007 .

[94]  Ali Azadeh,et al.  A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran , 2008 .

[95]  Susan Athey,et al.  Machine Learning Methods That Economists Should Know About , 2019, Annual Review of Economics.

[96]  H. Pao.  Comparing linear and nonlinear forecasts for Taiwan's electricity consumption , 2006 .

[97]  Maumita Bhattacharya,et al.  Intelligent Financial Fraud Detection: A Comprehensive Review , 2015 .

[98]  Brandyn Bok,et al.  Macroeconomic Nowcasting and Forecasting with Big Data , 2017 .

[99]  Mahour Mellat Parast,et al.  The impact of entrepreneurship orientation on project performance: A machine learning approach , 2020, International Journal of Production Economics.

[100]  Vadlamani Ravi,et al.  Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review , 2007, Eur. J. Oper. Res..

[101]  Chao-Hsien Chu,et al.  A Review of Data Mining-Based Financial Fraud Detection Research , 2007, 2007 International Conference on Wireless Communications, Networking and Mobile Computing.

[102]  R. Weron.  Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .

[103]  Jianping Li,et al.  A deep learning ensemble approach for crude oil price forecasting , 2017 .

[104]  Zhang Yang,et al.  Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods , 2017 .

[105]  Tshilidzi Marwala,et al.  The use of genetic algorithms and neural networks to approximate missing data in database , 2005, IEEE 3rd International Conference on Computational Cybernetics, 2005. ICCC 2005..

[106]  Ian T. Nabney,et al.  Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models , 2010 .

[107]  Bart De Schutter,et al.  Forecasting spot electricity prices Deep learning approaches and empirical comparison of traditional algorithms , 2018 .

[108]  Prabin Kumar Panigrahi,et al.  A Review of Financial Accounting Fraud Detection based on Data Mining Techniques , 2012, ArXiv.

[109]  Warren B. Powell,et al.  Adaptive Stochastic Control for the Smart Grid , 2011, Proceedings of the IEEE.

[110]  B. Handel,et al.  Wearable Technologies and Health Behaviors: New Data and New Methods to Understand Population Health. , 2017, The American economic review.

[111]  Vladimir Ceperic,et al.  Short-term forecasting of natural gas prices using machine learning and feature selection algorithms , 2017 .

[112]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..