Linda index forecast based on ARIMA time series

Cesar Hernández, Jesús Prieto, Diego Giral

Article ID: 7918
Vol 8, Issue 11, 2024

VIEWS - 0 (Abstract) 0 (PDF)

Abstract


Competition in the telecommunications market has significant benefits and impacts in various fields of society such as education, health and the economy. Therefore, it is key not only to monitor the behavior of the concentration of the telecommunications market but also to forecast it to guarantee an adequate level of competition. This work aims to forecast the Linda index of the telecommunications market based on an ARIMA time series model. To achieve this, we obtain data on traffic, revenue, and access from companies in the telecommunications market over a decade and use them to construct the Linda index. The Linda index allows us to measure the possible existence of oligopoly and the inequality between different market shares. The data is modeled through an ARIMA time series to finally predict the future values of the Linda index. The results show that the Colombian telecommunications market has a slight concentration that can affect the level of competition.


Keywords


ARIMA; concentration; Linda index; market; modeling; time series; telecommunications

Full Text:

PDF


References


Aguilar, D., Agüero, A., & Barrantes, R. (2020). Network effects in mobile telecommunications markets: A comparative analysis of consumers’ preferences in five Latin American countries. Telecommunications Policy, 44(5), 101972. https://doi.org/10.1016/j.telpol.2020.101972

Akinrotimi, A. O., Ogundokun, R. O., Mabayoje, M. A., et al. (2023). A Smote-Based Churn Prediction System Using Machine Learning Techniques. 2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), 11, 1–6. https://doi.org/10.1109/seb-sdg57117.2023.10124631

Alizadeh, M., Beheshti, M. T. H., Ramezani, A., et al. (2023). An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks. Transactions on Emerging Telecommunications Technologies, 34(12). Portico. https://doi.org/10.1002/ett.4860

Bardey, D., Sáenz, B., Aristizábal, D., et al. (2020). Impact of the concentration of the mobile market in Colombia on Competitiveness (Spanish). Available online: https://economia.uniandes.edu.co/sites/default/files/eventos/Estudio-movistar.pdf (accessed on 12 August 2024).

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., et al. (2016). Time Series Analysis: Forecasting and Control. John Wiley & Sons Inc.

Bravo Apolinario, Y., Quispe, D. K. P., Rodriguez, P. B. V., et al. (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

Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting, Second Edition. Available online: http://home.iitj.ac.in/~parmod/document/introduction time series.pdf (accessed on 12 August 2024).

Chávez Quisbert, N. (1997). Arima Models. Revista Ciencia y Cultura, 1, 23-30.

Comisión de Regulación de Comunicaciones. (2023a). Postscript: Beyond the data (Spanish). Available online: https://postdata.gov.co/ (accessed on 12 August 2024).

Comisión de Regulación de Comunicaciones. (2023b). Battery of indicators for the analysis of competition in communications markets (Spanish). Available online: https://postdata.gov.co/story/bateria-de-indicadores-para-el-analisis-de-competencia (accessed on 12 August 2024).

Comisión de Regulación de las Comunicaciones. (2021). ICT and Postal Industry Report 2020 (Spanish). Available online: https://postdata.gov.co/ (accessed on 12 August 2024).

de Miera Berglind, O. S. (2015). Spectrum concentration and market competition. Implications for the use of caps in Mexico. In: Proceedings of the 2015 Conference of Telecommunication, Media and Internet Techno-Economics (CTTE). https://doi.org/10.1109/ctte.2015.7347228

Fuentes Fernández, S. (2023). Time Series: Arima Model Box-Jenkins Methodology (Spanish). Available online: https://www.estadistica.net/ECONOMETRIA/SERIES-TEMPORALES/modelo-arima.pdf (accessed on 12 August 2024).

Grand View Research. (2021). Telecom Services Market Size, Share & Trends Analysis Report by Service Type (Mobile Data Services, Machine-To-Machine Services), By Transmission (Wireline, Wireless). Available online: https://www.grandviewresearch.com/industry-analysis/global-telecom-services-market (accessed on 12 August 2024).

Kumari, S., & Muthulakshmi, P. (2024). SARIMA Model: An Efficient Machine Learning Technique for Weather Forecasting. Procedia Computer Science, 235, 656–670. https://doi.org/10.1016/j.procs.2024.04.064

Li, L., Huang, S., Ouyang, Z., Li, N. (2022). A Deep Learning Framework for Non-stationary Time Series Prediction. In: Proceedings of 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA); 20-22 May 2022; Changchun, China. https://doi.org/10.1109/cvidliccea56201.2022.9824863

Lis-Gutiérrez, J.-P. (2013). Market Concentration and Market Stability Measures: An Application for Excel (Market Concentration and Market Stability Measures: An Application for Excel) (Spanish). SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2279769

MathWorks, Inc. (2024). Deep learning time series forecasting (Spanish). Available online: https://la.mathworks.com/help/deeplearning/ug/time-series-forecasting-using-deep-learning.html (accessed on 12 August 2024).

Melnik, A., Shy, O., & Stenbacka, R. (2008). Assessing market dominance. Journal of Economic Behavior & Organization, 68(1), 63–72. https://doi.org/10.1016/j.jebo.2008.03.010

Mills, T. C. (2019). Time Series and Their Features. Applied Time Series Analysis, 1–12. https://doi.org/10.1016/b978-0-12-813117-6.00001-6

MinTIC. (2023). ICT Quarterly Newsletter February 2023 (Spanish). Available online: https://colombiatic.mintic.gov.co/679/articles-274258_archivo_pdf.pdf (accessed on 12 August 2024).

Nekmahmud, Md., & Rahman, S. (2018). Measuring the Competitiveness Factors in Telecommunication Markets. In: Khajeheian, D., Friedrichsen, M., & Mödinger, W. (editors). Competitiveness in Emerging Markets. Springer International Publishing. pp. 339–372.

Ray, S., Lama, A., Mishra, P., et al. (2023). An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique. Applied Soft Computing, 149, 110939. https://doi.org/10.1016/j.asoc.2023.110939

Wang, C. H., & Liu, C. C. (2022). Market competition, technology substitution, and collaborative forecasting for smartphone panels and supplier revenues. Computers & Industrial Engineering, 169, 108295. https://doi.org/10.1016/j.cie.2022.108295

Wang, H., Song, S., Zhang, G., et al. (2023). Predicting daily streamflow with a novel multi-regime switching ARIMA-MS-GARCH model. Journal of Hydrology: Regional Studies, 47, 101374. https://doi.org/10.1016/j.ejrh.2023.101374

Wang, X., Kang, Y., Hyndman, R. J., et al. (2023). Distributed ARIMA models for ultra-long time series. International Journal of Forecasting, 39(3), 1163–1184. https://doi.org/10.1016/j.ijforecast.2022.05.001

Yasmin, S., & Moniruzzaman, Md. (2024). Forecasting of area, production, and yield of jute in Bangladesh using Box-Jenkins ARIMA model. Journal of Agriculture and Food Research, 16, 101203. https://doi.org/10.1016/j.jafr.2024.101203

Zhang, J., Liu, H., Bai, W., et al. (2024). A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting. The North American Journal of Economics and Finance, 69, 102022. https://doi.org/10.1016/j.najef.2023.102022




DOI: https://doi.org/10.24294/jipd.v8i11.7918

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Cesar Hernández, Jesús Prieto, Diego Giral

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.