• DocumentCode
    3361885
  • Title

    Forecasting Mineral Commodity Prices with ARIMA-Markov Chain

  • Author

    Li, Yong ; Hu, Nailian ; Li, Guoqing ; Yao, Xulong

  • Author_Institution
    Sch. of Civil & Environ. Eengineering, Univ. of Sci. & Technol. Beijing, Beijing, China
  • Volume
    1
  • fYear
    2012
  • fDate
    26-27 Aug. 2012
  • Firstpage
    49
  • Lastpage
    52
  • Abstract
    Scientific prediction has an important significance for establishing industrial policy and making plan in economic market. For the purpose of forecasting mineral commodity price accurately, an ARIMA-Markov chain method is proposed based on the study of time series methods and stochastic process theory. In order to test the prediction effect of the proposed method, a case study is carried out through using mineral molybdenum price values as research data. The results of the case study indicate that the prediction precision of our proposed method is much higher and less limitation to prediction step length than ARIMA model. It is proven that ARIMA-Markov chain performs an excellent property for mineral molybdenum price prediction.
  • Keywords
    Markov processes; autoregressive moving average processes; forecasting theory; industrial economics; minerals; molybdenum; pricing; time series; ARIMA; Markov chain; economic market; industrial policy; mineral commodity price forecasting; mineral molybdenum price value; stochastic process theory; time series method; Forecasting; Markov processes; Mathematical model; Minerals; Predictive models; Time series analysis; ARIMA; Forecasting; Markov chain; Mineral commodity price;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2012 4th International Conference on
  • Conference_Location
    Nanchang, Jiangxi
  • Print_ISBN
    978-1-4673-1902-7
  • Type

    conf

  • DOI
    10.1109/IHMSC.2012.18
  • Filename
    6305622