Estimation of the LINDA index prediction based on deep learning models

Diego Armando Giral-Ramirez, Cesar Hernández, Ernesto Cadena-Muñoz

Article ID: 9003
Vol 8, Issue 15, 2024

VIEWS - 16 (Abstract) 3 (PDF)

Abstract


Recognizing the importance of competition analysis in telecommunications markets is essential to improve conditions for users and companies. Several indices in the literature assess competition in these markets, mainly through company concentration. Artificial Intelligence (AI) emerges as an effective solution to process large volumes of data and manually detect patterns that are difficult to identify. This article presents an AI model based on the LINDA indicator to predict whether oligopolies exist. The objective is to offer a valuable tool for analysts and professionals in the sector. The model uses the traffic produced, the reported revenues, and the number of users as input variables. As output parameters of the model, the LINDA index is obtained according to the information reported by the operators, the prediction using Long-Short Term Memory (LSTM) for the input variables, and finally, the prediction of the LINDA index according to the prediction obtained by the LSTM model. The obtained Mean Absolute Percentage Error (MAPE) levels indicate that the proposed strategy can be an effective tool for forecasting the dynamic fluctuations of the communications market.


Keywords


artificial intelligence; deep learning; LINDA index; long-short term memory; market prediction

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References


Adetunji, A. J., and Moses, B. O. (2022). The role of network technologies in the enhancement of the health, education, and energy sectors. Network and Communication Technologies, 7(1), 39.

Bardey, D., Becerra, A., and Cabrera, P. (2013). Análisis económico de la normativa de libre competencia en Colombia.

Beckert, W., and Siciliani, P. (2022). Protecting Sticky Consumers in Essential Markets. Review of Industrial Organization, 61(3), 247–278. https://doi.org/10.1007/s11151-022-09880-z

Berradi, Z., Lazaar, M., Mahboub, O., and Omara, H. (2020). A Comprehensive Review of Artificial Intelligence Techniques in Financial Market. 2020 6th IEEE Congress on Information Science and Technology (CiSt), 367–371. https://doi.org/10.1109/CiSt49399.2021.9357175

Comisión de Regulación de Comunicaciones - Republica de Colombia. (2024a). Postscript - Beyond the data (Spanish). Fixed-Rate Mobile Internet Subscribers, Revenues and Traffic. Available online: https://www.postdata.gov.co/dataset/suscriptores-ingresos-y-tráfico-de-internet-móvil-por-cargo-fijo (accessed on 1 July 2024).

Comisión de Regulación de Comunicaciones - Republica de Colombia. (2024b). Postscript - Beyond the data (Spanish). Mobile Internet Subscribers, Revenues and Traffic on Demand. Available online: https://www.postdata.gov.co/dataset/abonados-ingresos-y-tráfico-de-internet-móvil-por-demanda (accessed on 1 July 2024).

Das, S., Tariq, A., Santos, T., Kantareddy, S. S., and Banerjee, I. (2023). Recurrent neural networks (RNNs): architectures, training tricks, and introduction to influential research. Machine Learning for Brain Disorders, 117–138.

Ding, X., Lv, Q., Zou, Y., and Zhang, G. (2022). Spectrum Prediction for Satellite based Spectrum-Sensing Systems Using Deep Learning. GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 3472–3477. https://doi.org/10.1109/GLOBECOM48099.2022.10000832

Doğan, E. (2021). Performance analysis of LSTM model with multi-step ahead strategies for a short-term traffic flow prediction. Zeszyty Naukowe. Transport/Politechnika Śląska, 111.

Ghaffar Nia, N., Kaplanoglu, E., and Nasab, A. (2023). Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discover Artificial Intelligence, 3(1), 5.

Giral, D., Hernández, C., and Salgado, C. (2021). Spectral decision in cognitive radio networks based on deep learning. Expert Systems with Applications, 180, 115080. https://doi.org/10.1016/j.eswa.2021.115080

Giral-Ramírez, D. (2022). Intelligent Fault Location Algorithms for Distributed Generation Distribution Networks: A Review. PRZEGLĄD ELEKTROTECHNICZNY, 1(7), 139–146. https://doi.org/10.15199/48.2022.07.23

Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks (Vol. 385). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-24797-2

Graves, A., and Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5–6), 602–610. https://doi.org/10.1016/j.neunet.2005.06.042

He, A., Kyung Kyoon Bae, Newman, T. R., Gaeddert, J., Kyouwoong Kim, Menon, R., Morales-Tirado, L., Neel, J. J., Youping Zhao, Reed, J. H., and Tranter, W. H. (2010). A Survey of Artificial Intelligence for Cognitive Radios. IEEE Transactions on Vehicular Technology, 59(4), 1578–1592. https://doi.org/10.1109/TVT.2010.2043968

Hernández, C., López, D., and Giral, D. (2020). Modelo de decisión espectral colaborativo para mejorar el desempeño de las redes de radio cognitiva (Editorial UD (ed.); Primera Ed).

Hochreiter, S., and Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Li, L., Huang, S., Ouyang, Z., and Li, N. (2022). A Deep Learning Framework for Non-stationary Time Series Prediction. 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA), 339–342. https://doi.org/10.1109/CVIDLICCEA56201.2022.9824863

Lu, X.-Q., Tian, J., Liao, Q., Xu, Z.-W., and Gan, L. (2024). CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction. Journal of Electronic Science and Technology, 22(2), 100256. https://doi.org/https://doi.org/10.1016/j.jnlest.2024.100256

Ma, Z., Zhang, H., and Liu, J. (2023). MM-RNN: A Multimodal RNN for Precipitation Nowcasting. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–14. https://doi.org/10.1109/TGRS.2023.3264545

Myers, J. H., and Tauber, E. (2011). Market structure analysis. Marketing Classics Press.

Ochuba, N. A., Amoo, O. O., Akinrinola, O., Usman, F. O., and Okafor, E. S. (2024). Market expansion and competitive positioning in satellite telecommunications: a review of analytics-driven strategies within the global landscape. International Journal of Management & Entrepreneurship Research, 6(3), 567–581.

Prakash, S., Jalal, A. S., and Pathak, P. (2023). Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India. 2023 6th International Conference on Information Systems and Computer Networks (ISCON), 1–6. https://doi.org/10.1109/ISCON57294.2023.10112065

Qazi, A., and Al-Mhdawi, M. K. S. (2024). Exploring critical drivers of global innovation: A Bayesian Network perspective. Knowledge-Based Systems, 299, 112127. https://doi.org/https://doi.org/10.1016/j.knosys.2024.112127

Rithani, M., Kumar, R. P., and Doss, S. (2023). A review on big data based on deep neural network approaches. Artificial Intelligence Review, 56(12), 14765–14801.

Shi, J., Jain, M., and Narasimhan, G. (2022). Time series forecasting (tsf) using various deep learning models. ArXiv Preprint ArXiv:2204.11115.

Vardhan, B. V. S., Khedkar, M., and Thakre, P. (2022). A Comparative Analysis of Hold Out, Cross and Re-Substitution Validation in Hyper-Parameter Tuned Stochastic Short Term Load Forecasting. 2022 22nd National Power Systems Conference (NPSC), 448–453. https://doi.org/10.1109/NPSC57038.2022.10069288

Vardhan, B. V. S., Khedkar, M., Srivastava, I., Thakre, P., and Bokde, N. D. (2023). A Comparative Analysis of Hyperparameter Tuned Stochastic Short Term Load Forecasting for Power System Operator. Energies, 16(3), 1243. https://doi.org/10.3390/en16031243

Veeriah, V., Zhuang, N., and Qi, G.-J. (2015). Differential Recurrent Neural Networks for Action Recognition. 2015 IEEE International Conference on Computer Vision (ICCV), 4041–4049. https://doi.org/10.1109/ICCV.2015.460

Venegas, P. B., Porras Quispe, D. K., Bravo Apolinario, Y., and Camacho Gadea, M. J. (2022). Bank concentration, measured by different indicators. The peruvian case. Journal of Globalization, Competitiveness and Governability, 16(1). https://doi.org/10.3232/GCG.2022.V16.N1.05

Wang, G., Liu, Q., Yao, Y., and Skowron, A. (Eds.). (2003). Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (Vol. 2639). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-39205-X

Wang, X., and Zhang, Y. (2020). Multi-step-ahead time series prediction method with stacking LSTM neural network. 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD), 51–55.

Wu, J., and He, Y. (2021). Prediction of GDP in Time Series Data Based on Neural Network Model. 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID), 20–23. https://doi.org/10.1109/AIID51893.2021.9456509




DOI: https://doi.org/10.24294/jipd9003

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