Prediction of Macroeconomic Growth Using Backpropagation Algorithms: A Review

Nur Fitri Hidayanti; Syaharuddin; Nurul Hidayati Indra Ningsih; Ahmad Hulaimi; Zaenafi Ariani; Dedy Iswanto

doi:10.18535/ijsrm/v13i01.em07

Abstract

This study aims to evaluate the effectiveness of the backpropagation algorithm in predicting macroeconomic growth through a Systematic Literature Review (SLR) approach. The review analyzes literature sourced from reputable indexes such as Scopus, DOAJ, and Google Scholar, focusing on publications from the last decade (2013–2024). It examines various studies on the application of the backpropagation algorithm, including parameter settings and model selection that influence prediction accuracy. The findings indicate that careful parameter configuration, such as the number of neurons, hidden layers, and learning rates, along with appropriate model selection, significantly enhance the performance of the backpropagation algorithm in macroeconomic prediction. This study highlights that combining optimal techniques and accurate parameter configurations substantially improves prediction accuracy and efficiency. It provides valuable insights and practical guidance for researchers and practitioners in designing more reliable and effective macroeconomic prediction models.

References

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Syaharuddin. Time-Series Analysis in Financial Prediction : A Literature Review. Sainstek J Sains dan Teknol. 2024;16(2):58–67.Google Scholar ↗
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A DD, P A FT. Back Propagation. Int J Res Appl Sci Eng Technol. 2023;Google Scholar ↗
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Damasevicius R. Artificial Intelligence Techniques in Economic Analysis. Econ Anal Lett. 2023;Google Scholar ↗
Hermanto TI, Idrus A, Sugiyanta L, Nasution D, Gunawan I. Neural Network Back-Propagation Method as Forecasting Technique. In: Journal of Physics: Conference Series. 2022.Google Scholar ↗
Yang W, Jia C, Liu R. Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM. Secur Commun Networks. 2022;Google Scholar ↗
Hanafiah MA, Ginantra NLWSR, GS AD. Analysis of ANN Backpropagation Ability to Predict Expenditure Group Inflation. IJISTECH (International J Inf Syst Technol. 2020;Google Scholar ↗
Talakua MW, Ilwaru VYI, Tomasouw BP, Limba SZ. Inflation Forecasts In Ambon Using Neural Network Applications Backpropagation. BAREKENG J Ilmu Mat dan Terap. 2022;Google Scholar ↗
Syaharuddin, Pramita D, Nusantara T, Subanji. Forecasting Using Back Propagation with 2-Layers Hidden. In: Journal of Physics: Conference Series. 2021.Google Scholar ↗
Chen JM. Economic Forecasting With Autoregressive Methods and Neural Networks. SSRN Electron J. 2020;Google Scholar ↗
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Sun H, Yao Z, Miao Q. Design of Macroeconomic Growth Prediction Algorithm Based on Data Mining. Mob Inf Syst. 2021;Google Scholar ↗
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Pang C. Construction and Analysis of Macroeconomic Forecasting Model Based on Biclustering Algorithm. J Math. 2022;Google Scholar ↗
Giap CN, Ha DT, Huy VQ, Hien DTT, Son DT, Trang LM. Firm Performance Prediction for Macroeconomic Diffusion Index using Machine Learning. Int J Adv Comput Sci Appl. 2021;Google Scholar ↗
Khan MA, Abbas K, Su’ud MM, Salameh AA, Alam MM, Aman N, et al. Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach. Sustain. 2022;Google Scholar ↗
Meyer DF, Habanabakize T. An assessment of the value of PMI and manufacturing sector growth in predicting overall economic output (GDP) in South Africa. Int J Ebus eGovernment Stud. 2019;Google Scholar ↗
Okoro CO. Macroeconomic Factors And Stock Market Performance: Evidence From Nigeria. Int J Soc Sci Humanit Rev. 2017;Google Scholar ↗
Wiśniewski H. Panelowa weryfikacja wpływu zmiennych makroekonomicznych na indeksy giełdowe. Probl Zarz. 2017;Google Scholar ↗
Som BK, Goel H. Analyzing Dependence of Key Macroeconomic Variables on BSE Using Regression. Int J Appl Behav Econ. 2022;Google Scholar ↗
Ororbia AG, Mali A, Kifer D, Lee Giles C. Backpropagation-Free Deep Learning with Recursive Local Representation Alignment. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. 2023.Google Scholar ↗
Li G, Zrimec J, Ji B, Geng J, Larsbrink J, Zelezniak A, et al. Performance of Regression Models as a Function of Experiment Noise. Bioinform Biol Insights. 2021;Google Scholar ↗
Zhu F, Cai Z, Peng L. Predictive regressions for macroeconomic data. Ann Appl Stat. 2014;Google Scholar ↗
38. Roger L. Foreign Aid, Poor Data, and the Fragility of Macroeconomic Inference. SSRN Electron J. 2016;Google Scholar ↗
Hašková S. An alternative approach for estimating GDP growth rate: fuzzy prediction model. ACC J. 2019;Google Scholar ↗
Yang S, Puggioni G, Harlow LL, Redding CA. A comparison of different methods of zero - inflated data analysis and an application in health surveys. J Mod Appl Stat Methods. 2017;Google Scholar ↗
Jaiswal JK, Das R. Application of artificial neural networks with backpropagation technique in the financial data. In: IOP Conference Series: Materials Science and Engineering. 2017.Google Scholar ↗
Suwandi WS. Do Economic Growth, Income Distribution, and Investment Reduce Poverty Level? J Berk Ilm Efisiensi. 2022;Google Scholar ↗
Das PK, Das PK. Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach. J Quant Econ. 2024;Google Scholar ↗
Chen S, Han X, Shen Y, Ye C. Application of Improved LSTM Algorithm in Macroeconomic Forecasting. Comput Intell Neurosci. 2021;Google Scholar ↗
Zhang Z. Prediction of Economic Operation Index Based on Support Vector Machine. Mob Inf Syst. 2022;Google Scholar ↗
Chaudhuri A, De K. Fuzzy Support Vector Machine for bankruptcy prediction. In: Applied Soft Computing Journal. 2011.Google Scholar ↗
Mustafidah H, Suwarsito S. Performance of Levenberg-Marquardt Algorithm in Backpropagation Network Based on the Number of Neurons in Hidden Layers and Learning Rate. JUITA J Inform. 2020;Google Scholar ↗
Abdoli G. Comparing the Prediction Accuracy of LSTM and ARIMA Models for Time-Series with Permanent Fluctuation. SSRN Electron J. 2020;Google Scholar ↗
Tang L. Application of Nonlinear Autoregressive with Exogenous Input (Narx) Neural Network in Macroeconomic Forecasting, National Goal Setting and Global Competitiveness Assessment. SSRN Electron J. 2020;Google Scholar ↗
Zhang Q, Abdullah AR, Chong CW, Ali MH. A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy. Comput Intell Neurosci. 2022;Google Scholar ↗

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[refR-2] Syaharuddin. Time-Series Analysis in Financial Prediction : A Literature Review. Sainstek J Sains dan Teknol. 2024;16(2):58–67.Google Scholar ↗

[refR-3] Feng Y. Design of Financial Data Evaluation System under Neural Network Algorithm. In: 3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023. 2023.Google Scholar ↗

[refR-4] Perwira Negara HR. Computational Modeling of ARIMA-based G-MFS Methods: Long-term Forecasting of Increasing Population. Int J Emerg Trends Eng Res. 2020;8(7):3665–9.Google Scholar ↗

[refR-5] Soewignjo S, Sediono, Mardianto MFF, Pusporani E. Prediksi Harga Saham Bank BCA (BBCA) Pasca Stock Split dengan Artificial Neural Network dengan Algoritma Backpropagation. G-Tech J Teknol Terap. 2023;Google Scholar ↗

[refR-6] Ozbay S. Modified Backpropagation Algorithm with Multiplicative Calculus in Neural Networks. Elektron ir Elektrotechnika. 2023;Google Scholar ↗

[refR-7] A DD, P A FT. Back Propagation. Int J Res Appl Sci Eng Technol. 2023;Google Scholar ↗

[refR-8] Wu W-Y. A Back-Propagation Neural Network for Recognizing Objects. Eur J Eng Technol Res. 2022;Google Scholar ↗

[refR-9] Damasevicius R. Artificial Intelligence Techniques in Economic Analysis. Econ Anal Lett. 2023;Google Scholar ↗

[refR-10] Hermanto TI, Idrus A, Sugiyanta L, Nasution D, Gunawan I. Neural Network Back-Propagation Method as Forecasting Technique. In: Journal of Physics: Conference Series. 2022.Google Scholar ↗

[refR-11] Yang W, Jia C, Liu R. Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM. Secur Commun Networks. 2022;Google Scholar ↗

[refR-12] Hanafiah MA, Ginantra NLWSR, GS AD. Analysis of ANN Backpropagation Ability to Predict Expenditure Group Inflation. IJISTECH (International J Inf Syst Technol. 2020;Google Scholar ↗

[refR-13] Talakua MW, Ilwaru VYI, Tomasouw BP, Limba SZ. Inflation Forecasts In Ambon Using Neural Network Applications Backpropagation. BAREKENG J Ilmu Mat dan Terap. 2022;Google Scholar ↗

[refR-14] Syaharuddin, Pramita D, Nusantara T, Subanji. Forecasting Using Back Propagation with 2-Layers Hidden. In: Journal of Physics: Conference Series. 2021.Google Scholar ↗

[refR-15] Chen JM. Economic Forecasting With Autoregressive Methods and Neural Networks. SSRN Electron J. 2020;Google Scholar ↗

[refR-16] Cheng F, Fu Z. Macroeconomic Forecasting Based on Mixed Frequency Vector Autoregression and Neural Network Models. Wirel Commun Mob Comput. 2022;Google Scholar ↗

[refR-17] Haritha K, Shailesh S, Judy M V., Ravichandran KS, Krishankumar R, Gandomi AH. A novel neural network model with distributed evolutionary approach for big data classification. Sci Rep. 2023;Google Scholar ↗

[refR-18] Bjerva J, Kouw WM, Augenstein I. Back to the future – Sequential alignment of text representations. In: AAAI 2020 - 34th AAAI Conference on Artificial Intelligence. 2020.Google Scholar ↗

[refR-19] Arjona R, Nesseris S. What can machine learning tell us about the background expansion of the Universe? Phys Rev D. 2020;Google Scholar ↗

[refR-20] Goulet Coulombe P, Leroux M, Stevanovic D, Surprenant S. How is machine learning useful for macroeconomic forecasting? J Appl Econom. 2022;Google Scholar ↗

[refR-21] Zhang J, Wen J, Yang Z. China’s GDP forecasting using Long Short Term Memory Recurrent Neural Network and Hidden Markov Model. PLoS One. 2022;Google Scholar ↗

[refR-22] Zhang Q, Yan L, Hu R, Li Y, Hou L. Regional Economic Prediction Model Using Backpropagation Integrated with Bayesian Vector Neural Network in Big Data Analytics. Comput Intell Neurosci. 2022;Google Scholar ↗

[refR-23] Lahmiri S. An exploration of backpropagation numerical algorithms in modeling US exchange rates. In: Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications. 2016.Google Scholar ↗

[refR-24] Sun H, Yao Z, Miao Q. Design of Macroeconomic Growth Prediction Algorithm Based on Data Mining. Mob Inf Syst. 2021;Google Scholar ↗

[refR-25] Hutagalung SV, Yennimar Y, Rumapea ER, Hia MJG, Sembiring T, Manday DR. Comparison Of Support Vector Regression And Random Forest Regression Algorithms On Gold Price Predictions. J Sist Inf dan Ilmu Komput Prima(JUSIKOM PRIMA). 2023;Google Scholar ↗

[refR-26] Farhanuddin, Sarah Ennola Karina Sihombing, Yahfizham. Komparasi Multiple Linear Regression dan Random Forest Regression Dalam Memprediksi Anggaran Biaya Manajemen Proyek Sistem Informasi. J Comput Digit Bus. 2024;3(2):86–97.Google Scholar ↗

[refR-27] Singarimbun RN, Putra OE, Ginantra NLWSR, Dewi MP. Backpropagation Artificial Neural Network Enhancement using Beale-Powell Approach Technique. In: Journal of Physics: Conference Series. 2022.Google Scholar ↗

[refR-28] Pang C. Construction and Analysis of Macroeconomic Forecasting Model Based on Biclustering Algorithm. J Math. 2022;Google Scholar ↗

[refR-29] Giap CN, Ha DT, Huy VQ, Hien DTT, Son DT, Trang LM. Firm Performance Prediction for Macroeconomic Diffusion Index using Machine Learning. Int J Adv Comput Sci Appl. 2021;Google Scholar ↗

[refR-30] Khan MA, Abbas K, Su’ud MM, Salameh AA, Alam MM, Aman N, et al. Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach. Sustain. 2022;Google Scholar ↗

[refR-31] Meyer DF, Habanabakize T. An assessment of the value of PMI and manufacturing sector growth in predicting overall economic output (GDP) in South Africa. Int J Ebus eGovernment Stud. 2019;Google Scholar ↗

[refR-32] Okoro CO. Macroeconomic Factors And Stock Market Performance: Evidence From Nigeria. Int J Soc Sci Humanit Rev. 2017;Google Scholar ↗

[refR-33] Wiśniewski H. Panelowa weryfikacja wpływu zmiennych makroekonomicznych na indeksy giełdowe. Probl Zarz. 2017;Google Scholar ↗

[refR-34] Som BK, Goel H. Analyzing Dependence of Key Macroeconomic Variables on BSE Using Regression. Int J Appl Behav Econ. 2022;Google Scholar ↗

[refR-35] Ororbia AG, Mali A, Kifer D, Lee Giles C. Backpropagation-Free Deep Learning with Recursive Local Representation Alignment. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023. 2023.Google Scholar ↗

[refR-36] Li G, Zrimec J, Ji B, Geng J, Larsbrink J, Zelezniak A, et al. Performance of Regression Models as a Function of Experiment Noise. Bioinform Biol Insights. 2021;Google Scholar ↗

[refR-37] Zhu F, Cai Z, Peng L. Predictive regressions for macroeconomic data. Ann Appl Stat. 2014;Google Scholar ↗

[refR-38] 38. Roger L. Foreign Aid, Poor Data, and the Fragility of Macroeconomic Inference. SSRN Electron J. 2016;Google Scholar ↗

[refR-39] Hašková S. An alternative approach for estimating GDP growth rate: fuzzy prediction model. ACC J. 2019;Google Scholar ↗

[refR-40] Yang S, Puggioni G, Harlow LL, Redding CA. A comparison of different methods of zero - inflated data analysis and an application in health surveys. J Mod Appl Stat Methods. 2017;Google Scholar ↗

[refR-41] Jaiswal JK, Das R. Application of artificial neural networks with backpropagation technique in the financial data. In: IOP Conference Series: Materials Science and Engineering. 2017.Google Scholar ↗

[refR-42] Suwandi WS. Do Economic Growth, Income Distribution, and Investment Reduce Poverty Level? J Berk Ilm Efisiensi. 2022;Google Scholar ↗

[refR-43] Das PK, Das PK. Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach. J Quant Econ. 2024;Google Scholar ↗

[refR-44] Chen S, Han X, Shen Y, Ye C. Application of Improved LSTM Algorithm in Macroeconomic Forecasting. Comput Intell Neurosci. 2021;Google Scholar ↗

[refR-45] Zhang Z. Prediction of Economic Operation Index Based on Support Vector Machine. Mob Inf Syst. 2022;Google Scholar ↗

[refR-46] Chaudhuri A, De K. Fuzzy Support Vector Machine for bankruptcy prediction. In: Applied Soft Computing Journal. 2011.Google Scholar ↗

[refR-47] Mustafidah H, Suwarsito S. Performance of Levenberg-Marquardt Algorithm in Backpropagation Network Based on the Number of Neurons in Hidden Layers and Learning Rate. JUITA J Inform. 2020;Google Scholar ↗

[refR-48] Abdoli G. Comparing the Prediction Accuracy of LSTM and ARIMA Models for Time-Series with Permanent Fluctuation. SSRN Electron J. 2020;Google Scholar ↗

[refR-49] Tang L. Application of Nonlinear Autoregressive with Exogenous Input (Narx) Neural Network in Macroeconomic Forecasting, National Goal Setting and Global Competitiveness Assessment. SSRN Electron J. 2020;Google Scholar ↗

[refR-50] Zhang Q, Abdullah AR, Chong CW, Ali MH. A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy. Comput Intell Neurosci. 2022;Google Scholar ↗