Abstract
The continuous evolution of economy hinders the decision-making process in this field. The former requires sophisticated techniques, and thus, manual and empirical methods are becoming increasingly obsolete. In this paper, we propose a computationally-supported approach that performs an economic profiling of cities based on their economic features and also a prediction of the future evolution of these economical metrics. Our contributions include (I) a data-ingestion module to extract, transform and load data, (II) a profiling module that achieves an unsupervised classification via a new distance-based cellular genetic algorithm and K-means and (III) a prediction unit based on long short-term memory artificial neural networks. Our proposal is tested on Spain, analysing all its 52 cities, where we use 33 types of real-world economic data that have been recorded monthly for fifteen years. All data has been obtained from the Spanish National Institute of Statistics. Our experiments show that the 52 cities could be clustered into only three economic profiles. This decrease in the complexity of entities to be considered allows managers at several levels and countries to take faster and more accurate decisions by dealing with few profiles rather than treating each city apart. Also, we found that each profile contains repetitive similarity patterns that are not only determined by economics but also indirectly ruled by the cities’ geo and demographic situations. Results also showed our prototype’s promising economic predictions.
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References
Bonaccorso, G.: Mastering Machine Learning Algorithms: Expert Techniques for Implementing Popular Machine Learning Algorithms, Fine-Tuning Your Models, and Understanding How They Work, 2nd edn. Packt Publishing, Birmingham (2020)
Chakrabarty, N., Rana, S., Chowdhury, S., Maitra, R.: RBM based joke recommendation system and joke reader segmentation. In: Deka, B., Maji, P., Mitra, S., Bhattacharyya, D.K., Bora, P.K., Pal, S.K. (eds.) PReMI 2019. LNCS, vol. 11942, pp. 229–239. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34872-4_26
Dahi, Z.A., Alba, E.: The grid-to-neighbourhood relationship in cellular GAs: from design to solving complex problems. Soft. Comput. 24(5), 3569–3589 (2019). https://doi.org/10.1007/s00500-019-04125-w
Dahi, Z.A., Luque, G., Alba, E.: Database, source code and results of the proposed machine learning-based approach for economics-tailored applications. https://github.com/Zakaria-Dahi/ML-Economis.git. Accessed 09 Feb 2022
El-Shorbagy, M.A., Ayoub, A.Y., Mousa, A.A., El-Desoky, I.M.: An enhanced genetic algorithm with new mutation for cluster analysis. Comput. Stat. 34(3), 1355–1392 (2019). https://doi.org/10.1007/s00180-019-00871-5
Istiake Sunny, M.A., Maswood, M.M.S., Alharbi, A.G.: Deep learning-based stock price prediction using LSTM and bi-directional LSTM model. In: 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), pp. 87–92 (2020)
Kimball, R., Caserta, J.: The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming and Delivering Data. Wiley, Hoboken (2011)
Ozden, E., Guleryuz, D.: Optimized machine learning algorithms for investigating the relationship between economic development and human capital. Comput. Econ. (2021)
Pérez-Ortega, J., Almanza-Ortega, N.N., Vega-Villalobos, A., Pazos-Rangel, R., Zavala-Díaz, C., Martínez-Rebollar, A.: The K-means algorithm evolution. In: Sud, K., Erdogmus, P., Kadry, S. (eds.) Introduction to Data Science and Machine Learning (chap. 5). IntechOpen, Rijeka (2020)
Whitley, L.D.: Cellular genetic algorithms. In: Proceedings of the 5th International Conference on Genetic Algorithms, p. 658. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Wu, J.: Advances in K-Means Clustering: A Data Mining Thinking. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29807-3
Yamarone, R.: The Trader’s Guide to Key Economic Indicators. Bloomberg Financial Series, 3rd edn. Wiley, Hoboken (2012)
Acknowledgements
This work resulted from a stay at the University of Malaga (Spain) using the grant “stays of researchers with prestigious recognition” and is also partially funded by the Universidad de Málaga, Consejería de Economía y Conocimiento de la Junta de Andalucía and FEDER under grant number UMA18-FEDERJA-003 (PRECOG); under grant PID 2020-116727RB-I00 (HUmove) funded by MCIN/AEI/10.13039/501100011033; and TAILOR ICT-48 Network (No. 952215) funded by EU Horizon 2020 research and innovation programme. Furthermore, the views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission.
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Dahi, Z.A., Luque, G., Alba, E. (2022). A Machine Learning-Based Approach for Economics-Tailored Applications: The Spanish Case Study. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_36
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