Е-Публикации
Технически университет - София

Abstract: The presence of complex time series and multimodal data in economics and finance necessitates the development of advanced analytical models capable of interpreting complex patterns and dynamics. Despite their fast development as LLM, the current AI systems cannot do that. This paper introduces how a novel computational architecture, NeuDen, which combines the strengths of evolving spiking neural networks (eSNNs) with the interpretability of evolving dynamic neuro-fuzzy systems (eDNFS), can be used to model high-dimensional temporal data in economics. The NeuDen model is applied to the analysis of remittance inflows, demonstrating its ability to not only model data but also uncover the underlying trends in intricate remittance data. This application highlights the model's potential as a tool for efficient modelling of economic data that also helps understand the dynamics of economic processes.

References

1. Ratha, D.: Remittances: a lifeline for development. Int. Monetary Fund Finan. Dev. 42, 4 (2005)
2. Perez-Saiz, H., Dridi, J., Gursoy, T., Bari, M.: The impact of remittances on economic activity: the importance of sectoral linkages. Int. Monetary Fund 175 (2019). https://doi.org/10.5089/9781498324489.001
3. Oeking, A., Choo, C.Y.: Coming home: are remittances in the ASEAN+3 another victim of the pandemic? (2020). https://amro-asia.org/coming-home-are-remittances-in-the-asean3-another-victim-of-the-pandemic/
4. Hossain, M., Abdulla, F., Majumder, A.: Comparing the forecasting performance of ARIMA and neural network model by using the remittances of Bangladesh. Jahangirnagar Univ. J. Stat. Stud. 34, 1–12 (2017)
5. Nyoni, T.: Modelling and Forecasting Remittances in Bangladesh Using The Box-Jenkins ARIMA Methodology (2019)
6. Meyer, D., Shera, A.: The impact of Remittances on Economic growth: an econometric model. Economi A.18 (2016). https://doi.org/10.1016/j.econ.2016.06.001
7. Chami, R., Fullenkamp, C., Jahjah, S.: Are immigrant remittance flows a source of capital for development? IMF Staff Papers 52(1), 55–81 (2005). http://www.jstor.org/stable/30035948
8. Abouhassan, I., Kasabov, N.: NeuDen: a framework for the integration of neuromorphic evolving spiking neural networks with dynamic evolving neuro-fuzzy systems for predictive and explainable modelling of streaming data. Evolving Syst. 16, 3 (2025). https://doi.org/10.1007/s12530-024-09630-4
9. Ouliaris, S.: What are economic models. Int. Monetary Fund Finan. Dev. 48(2) (2011)
10. Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. 2nd ed. Springer, New York (2002).
11. Box, G., Jenkins, G., Reinsel, G., Ljung, G.: Time series analysis: forecasting and control. J. Time Ser. Anal. 5th ed., Wiley and Sons Inc., Hoboken, New Jersey, p. 712 (2015). https://doi.org/10.1111/jtsa.12194
12. Aminian, F., Suarez, E.D., Aminian, M., et al.: Forecasting economic data with neural networks. Comput. Econ. 28, 71–88 (2006). https://doi.org/10.1007/s10614-006-9041-7
13. Herbrich, R., Keilbach, M., Graepel, T., Bollmann-Sdorra, P., Obermayer, K.: Neural networks in economics. In: Brenner, T. (eds.) Computational Techniques for Modelling Learning in Economics. Advances in Computational Economics, vol. 11. Springer, Boston, MA (1999). https://doi.org/10.1007/978-1-4615-5029-7_7
14. Li, Y., Ma, W.: Applications of artificial neural networks in financial economics: a survey. In: International Symposium on Computational Intelligence and Design, Hangzhou, China, pp. 211–214 (2010). https://doi.org/10.1109/ISCID.2010.70
15. Kaastra, I., Boyd, M.: Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3), 215–236 (1996). https://doi.org/10.1016/0925-2312(95)00039-9
16. Thawornwong, S., Enke, D.: The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing 56, 205–232 (2004). https://doi.org/10.1016/j.neucom.2003.05.001
17. Abouhassan, I., Kasabov, N., Jagtap, V., et al.: Spiking neural networks for predictive and explainable modelling of multimodal streaming data with a case study on financial time series and online news. Sci. Rep. 13(18367), 2023 (2023). https://doi.org/10.1038/s41598-023-42605-0
18. Falat, F., Pancikova, L.: Quantitative modelling in economics with advanced artificial neural networks. Proc. Econ. Finan. 34, 194–201 (2015). https://doi.org/10.1016/S2212-5671(15)01619-6
19. Reid, D., Hussain, A.J., Tawfik, H.: Financial time series prediction using spiking neural networks. PLoS ONE 9(8), e103656 (2014). https://doi.org/10.1371/journal.pone.0103656
20. Widiputra, H., Pears, R., Serguieva, A., Kasabov, N.: Dynamic interaction networks in modelling and predicting the behaviour of multiple interactive stock markets. Intell. Syst. Account. Finan. Manag. 16, 189–205 (2008). https://doi.org/10.1002/isaf.300
21. Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. Assoc. Comput. Mach. (2020). https://doi.org/10.1145/3394486.3403118.ISBN 9781450379984
22. Zhang, S., Tong, H., Xu, J., et al.: Graph convolutional networks: a comprehensive review. Comput. Soc. Netw. 6, 11 (2019). https://doi.org/10.1186/s40649-019-0069-y
23. Jang, J.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993). https://doi.org/10.1109/21.256541
24. Mitra, S., Hayashi, Y.: Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans. Neural Netw. 11(3), 748–768 (2000)
25. Powers, D.: Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation. Mach. Learn. Technol 2 (2008)
26. AbouHassan, I., Kasabov, N., et al.: ePAMeT: evolving Predictive Associative Memories for Time series. Evolving Syst. 16, 6 (2025). https://doi.org/10.1007/s12530-024-09628-y
27. Smith, A.: An Inquiry into the Nature and Causes of the Wealth of Nations, McMaster University Archive for the History of Economic Thought (1776)
28. Kasabov, N.: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence. Springer Series on Bio - and Neurosystems (SSBN, volume 7) (2019). https://doi.org/10.1007/978-3-662-57715-8
29. Kasabov, N.: The ECOS framework and the ECO learning method for evolving connectionist systems. J. Adv. Comput. Intell. 2(6), 195–202 (1998)
30. Kasabov, N.: NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw. 52, 62–76 (2014). https://doi.org/10.1016/j.neunet.2014.01.006. Elsevier
31. Kasabov, N., Song, Q.: DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans. Fuzzy Syst. 10, 144–154 (2002). https://doi.org/10.1109/91.995117
32. Kasabov, N.: Evolving fuzzy neural networks-algorithms, applications and biological motivation. In: Methodologies for the Conception, Design and Application of Soft Computing, pp. 271–274 (1998)
33. Kasabov, N.: Adaptation and interaction in dynamical systems: modelling and rule discovery through evolving connectionist systems. Appl. Soft Comput. 6, 307–322 (2006). https://doi.org/10.1016/j.asoc.2005.01.006
34. Kasabov, N., Dhoble, K., Nuntalid, N., Indiveri, G.: Dynamic evolving spiking neural networks for online spatio - and spectro-temporal pattern recognition. Neural Netw. 41, 188–201 (2013). https://doi.org/10.1016/j.neunet.2012.11.014
35. Schliebs, S., Kasabov, N.: Computational modelling with spiking neural networks. Springer Handbook of Bio-/Neuro Informatics, pp. 625–646 (2014)
36. Abouhassan, I., Kasabov, N., Popov, G., Trifonov, R.: Why use evolving neuro-fuzzy and spiking neural networks for incremental and explainable learning of time series? a case study on predictive modelling of trade imports and outlier detection. In: IEEE 11th International Conference on Intelligent Systems (IS), Warsaw, Poland, pp. 1–7 (2022). https://doi.org/10.1109/IS57118.2022.10019673
37. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985)
38. World Bank - KNOWMAD Homepage. https://www.knomad.org/data/remittances. Accessed 6 April 2024
39. United Nations Department of Economic and Social Affairs, Population Division (2020). International Migrant Stock (2020). https://www.un.org/development/desa/pd/content/international-migrant-stock. Accessed 6 April 2024
40. International Financial Statistics Homepage. https://data.imf.org/regular.aspx?key=61545850. Accessed 6 April 2024
41. Central Administration of Statistics Homepage. http://www.cas.gov.lb/index.php/national-accounts-en. Accessed 6 April 2024
42. Kelsen, B.A., Sumich, A., Kasabov, N., Liang, S.H.Y., Wang, G.Y.: What has social neuroscience learned from hyper-scanning studies of spoken communication? a systematic review. Neurosci. Biobehav. Rev. 132, 1249–1262 (2022). https://doi.org/10.1016/j.neubiorev.2020.09.008. ISSN 0149-7634
43. Kumarasinghe, K., Kasabov, N., Taylor, D.: Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements. Sci. Rep. 11(1), 2486 (2021). https://doi.org/10.1038/s41598-021-81805-4
44. Tu, E., Cao, L., Yang, J., Kasabov, N.: A novel graph-based k-means for nonlinear manifold clustering and representative selection. Neurocomputing 143, 109–122 (2014). https://doi.org/10.1016/j.neucom.2014.05.067
45. Kasabov, N.K.: STAM-SNN: spatio-temporal associative memories in brain-inspired spiking neural networks: concepts and perspectives. In: Kovács, L., Heidegger, T., Szakál, A. (eds.) Recent Advances in Intelligent Engineering. Topics in Intelligent Engineering and Informatics, vol. 18. Springer (2024). https://doi.org/10.1007/978-3-031-58257-8_1
46. Kasabov, N.K.: Life-long learning and evolving associative memories in brain-inspired spiking neural networks. MOJ App. Bio. Biomech. 8(1), 56–57 (2024). https://doi.org/10.15406/mojabb.2024.08.00208
47. Knowledge Engineering Homepage. http://www.knowledgeengineering.ai
48. NeuCube Homepage. http://www.kedri.aut.ac.nz/neucube
49. NeuCom Homepage. http://www.theneucom.com

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