Neural Networks Based Forecasting for Romanian Clothing Sector



Clothing industry enjoys a high level of attention on all world markets, despite the prolonged economic crisis. Companies have turned to knowledge and research, processing and analyzing information obtained from the market analysis, surveys, their own and their competitor’s sales evolution, and are making use of short- and medium-term forecasts as powerful tools for the top management. The paper presents a twofold approach regarding forecasting of the financial indicators and trends related to the Romanian clothing industry, firstly at macroeconomic level, taking into account the interest of potential investors in this field, and secondly at microeconomic level, representing the analysis of the results for an operational company.


Clothing industry Forecasting software Financial indicators 


  1. 1. Latest accessed 8 Dec 2012
  2. 2.
    Banica L, Pirvu D (2012) Intelligent financial forecasting, the key for a successful management. Int J Acad Res Accounting Finance Manage Sci 2(3):192–206. ISSN: 2225–8329Google Scholar
  3. 3.
    Yu Y, Choi T-M, Hui C-L (2011) An intelligent fast sales forecasting model for fashion products. Expert Syst Appl 38(6):7373–7379CrossRefGoogle Scholar
  4. 4.
    Christos S, Dimitrios S (1996) Neural networks.
  5. 5.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Avrigeanu AF, Anghel FG (2012) Romanian clothing industry: external market vs. internal market. Rom Econ Bus Rev 6(1):67–76Google Scholar
  8. 8.
    Georgescu G (2012) The global crisis impact on Romanian trade structure. Available online at
  9. 9.
    Romanian International Trade Yearbook (2011) Institute National of Statistics Romania. pp36–37Google Scholar
  10. 10.
  11. 11.
    Secrieru A (2013) The most popular Romanian export goods. MPRA_paper_36339.pdf
  12. 12.
    Masson R, Iosif L, MacKerron G, Fernie J (2007) Managing complexity in agile global fashion industry supply chains. Int J Logist Manage 18(2):238–254CrossRefGoogle Scholar
  13. 13.
    Fashion and clothing retail sector in Romania – a FRD center market entry services Romania report, 2010. Available online at
  14. 14.
  15. 15.
    Kotorov R (2009) Enhancing decision-making, cost-efficiency, and profitability with predictive analytics. Information BuildersGoogle Scholar
  16. 16.
    Walczak S (2001) An empirical analysis of data requirements for financial forecasting with neural networks. J Manage Inform Syst 17(4):203–222Google Scholar
  17. 17.
    Banica L, Pirvu D, Hagiu A (2012) Financial forecasting using neural networks. Int J Adv Manage Econ 1(6):70–79, Vol. Xxx, ISSN: 2278–3369Google Scholar
  18. 18.
    Khashei M, Bijari M (2010) An artificial neural network (p, d, q) model for timeseries forecasting. Expert Syst Appl 37(1):479–485CrossRefGoogle Scholar
  19. 19.
    Zhang G, Patuwo BE, Hu YM (1998) Forecasting with artificial neural networks. Int J Forecast 14:35–62CrossRefGoogle Scholar
  20. 20.
    Aamodt R (2010) Using artificial neural networks to forecast financial time series. Latest accessed 4 Dec 2012
  21. 21.
    Chirita M (2012) Usefulness of artificial neural networks for predicting financial and economic crisis. Annals of “Dunarea de Jos”, University of Galati, Fascicle I. Econ Appl Inform XVIII(2):61–66Google Scholar
  22. 22.
    Khashei M, Bijari M (2011) Which methodology is better for combining linear and nonlinear models for time series forecasting? J Ind Syst Eng 4(4):265–285Google Scholar
  23. 23.
    Filik UB, Kurban M (2007) A new approach for the short-term load forecasting with autoregressive and artificial neural network models. Int J Comput Intell Res 3(1):66–71Google Scholar
  24. 24.
    Elder J (2012) The best and the worst of predictive analytics: predictive modeling methods and common data mining mistakes, Elder Research, Inc. Workshop.
  25. 25.
    Xia M, Chu W (2010) Adaptive neural network model for time-series forecasting. Eur J Oper Res 207(2):807–816CrossRefGoogle Scholar
  26. 26.
  27. 27.
    Marchetti S, Tzavidis N, Pratesi M (2010) Non-parametric bootstrap mean squared error estimation for M-quantile estimators of small area averages, quantiles and poverty indicators. Comput Stat Data Anal 56(10):2889–2902CrossRefGoogle Scholar
  28. 28.
  29. 29.
    Pedroni M (2010) From fashion forecasting to coolhunting. Previsional models in fashion and in cultural production. Available online at _to_Coolhunting_ Previsional_Models_in_Fashion_and_in_Cultural_Production

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Faculty of EconomicsUniversity of PitestiPitestiRomania

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