IDEAS home Printed from https://ideas.repec.org/p/ulb/ulbeco/2013-13836.html
   My bibliography  Save this paper

Forecasting in the analysis of mobile telecommunication data: correction for outliers and replacement of missing observations

Author

Listed:
  • Rajae Azrak
  • Guy Melard
  • Hassane Njimi

Abstract

No abstract is available for this item.

Suggested Citation

  • Rajae Azrak & Guy Melard & Hassane Njimi, 2003. "Forecasting in the analysis of mobile telecommunication data: correction for outliers and replacement of missing observations," ULB Institutional Repository 2013/13836, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ulb:ulbeco:2013/13836
    Note: Conference paper presented at: (11-13 December 2003: Université Mohammed V - Agdal et LAB.SIR - Ecole Mohammedia d'ingénieurs, Rabat)
    as

    Download full text from publisher

    File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/13836/1/TR0445.pdf
    File Function: TR0445
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tych, Wlodek & Pedregal, Diego J. & Young, Peter C. & Davies, John, 2002. "An unobserved component model for multi-rate forecasting of telephone call demand: the design of a forecasting support system," International Journal of Forecasting, Elsevier, vol. 18(4), pages 673-695.
    2. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
    3. Fildes, Robert & Kumar, V., 2002. "Telecommunications demand forecasting--a review," International Journal of Forecasting, Elsevier, vol. 18(4), pages 489-522.
    4. Guy Melard & Jean-Michel Pasteels, 2000. "Automatic ARIMA modeling including interventions, using time series expert software," ULB Institutional Repository 2013/13744, ULB -- Universite Libre de Bruxelles.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rajae Azrak & Guy Melard & Hassane Njimi, 2004. "Forecasting in the analysis of mobile telecommunication data: correction for outliers and replacement of missing observations," ULB Institutional Repository 2013/13748, ULB -- Universite Libre de Bruxelles.
    2. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    3. Singh, Sarbjit & Parmar, Kulwinder Singh & Kumar, Jatinder & Makkhan, Sidhu Jitendra Singh, 2020. "Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Daniel Fernández, 2011. "Suficiencia del capital y previsiones de la banca uruguaya por su exposición al sector industrial," Monetaria, CEMLA, vol. 0(4), pages 517-589, octubre-d.
    5. Javier Pereda, 2011. "Estimación de la tasa natural de interés para Perú: un enfoque financiero," Monetaria, CEMLA, vol. 0(4), pages 429-459, octubre-d.
    6. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
    7. Rubio, Ginés & Pomares, Héctor & Rojas, Ignacio & Herrera, Luis Javier, 2011. "A heuristic method for parameter selection in LS-SVM: Application to time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 725-739, July.
    8. Singh, Sarbjit & Parmar, Kulwinder Singh & Makkhan, Sidhu Jitendra Singh & Kaur, Jatinder & Peshoria, Shruti & Kumar, Jatinder, 2020. "Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    9. George Athanasopoulos & D. Poskitt & Farshid Vahid, 2012. "Two Canonical VARMA Forms: Scalar Component Models Vis-à-Vis the Echelon Form," Econometric Reviews, Taylor & Francis Journals, vol. 31(1), pages 60-83.
    10. Tamara Burdisso & Eduardo Ariel Corso, 2011. "Incertidumbre y dolarización de cartera: el caso argentino en el último medio siglo," Monetaria, CEMLA, vol. 0(4), pages 461-515, octubre-d.
    11. Carlos Medel, 2012. "¿Akaike o Schwarz? ¿Cuál elegir para Predecir el PIB Chileno?," Working Papers Central Bank of Chile 658, Central Bank of Chile.
    12. Ediger, Volkan S. & Akar, Sertac & Ugurlu, Berkin, 2006. "Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model," Energy Policy, Elsevier, vol. 34(18), pages 3836-3846, December.
    13. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.
    14. Carlos A. Medel Vera, 2011. "¿Akaike o Schwarz? ¿Cuál utilizar para predecir el PIB chileno?," Monetaria, CEMLA, vol. 0(4), pages 591-615, octubre-d.
    15. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    16. Andrea BASTIANIN & Marzio GALEOTTI & Matteo MANERA, 2011. "Forecast evaluation in call centers: combined forecasts, flexible loss functions and economic criteria," Departmental Working Papers 2011-08, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    17. Mamadou-Diéne Diop & Jules Sadefo Kamdem, 2023. "Multiscale Agricultural Commodities Forecasting Using Wavelet-SARIMA Process," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 1-40, March.
    18. John Berdell & Animesh Ghoshal, 2015. "US–Mexico border tourism and day trips: an aberration in globalization?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 24(1), pages 1-18, December.
    19. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    20. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ulb:ulbeco:2013/13836. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Benoit Pauwels (email available below). General contact details of provider: https://edirc.repec.org/data/ecsulbe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.