• DocumentCode
    3174922
  • Title

    The Research on Combination Forecasting Model of the Automobile Sales Forecasting System

  • Author

    Gaojun, Liu ; Boxue, Long

  • Author_Institution
    Coll. of Inf. Eng., North China Univ. of Technol., Beijing, China
  • Volume
    3
  • fYear
    2009
  • fDate
    25-27 Dec. 2009
  • Firstpage
    82
  • Lastpage
    85
  • Abstract
    Automobile sells system plays an important role in automobile sales area, through the whole produce and management. Some forecast models have had unilateralism in some side nowadays, such as ARMA model. For example, the data of non-linearity has some error by ARMA model. This paper, assembles curve -regression model, Time Series Decomposition Model and RBF neural networks according to the weight distribution. Putting the Data Mining, math statistics and neural networks technique into automobile sales forecast system, It can improve the problem of unilateralism, The Combination forecasting model improves the veracity and utility range in automobile sales forecast. This paper, can also be used in which some other economic data that take on the obvious time character and trend in car-making and selling.
  • Keywords
    automobile industry; data mining; forecasting theory; marketing data processing; radial basis function networks; regression analysis; time series; RBF neural network; automobile sales forecasting system; combination forecasting model; curve regression model; data mining; math statistics; time series decomposition model; Assembly; Automobiles; Automotive engineering; Economic forecasting; Educational institutions; Marketing and sales; Neural networks; Predictive models; Technology forecasting; Vehicle dynamics; Combination forecasting model; Time series; automobile sales foreccating; the dynamics weight distribution;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-0-7695-3930-0
  • Electronic_ISBN
    978-1-4244-5423-5
  • Type

    conf

  • DOI
    10.1109/IFCSTA.2009.258
  • Filename
    5384739