Journal of Resources and Ecology ›› 2015, Vol. 6 ›› Issue (1): 60-64.DOI: 10.5814/j.issn.1674-764x.2015.01.008

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Forecasting the Coke Price Based on the Kalman Filtering Algorithm

ZHU Meifeng1,2   

  1. 1 Faculty of Statistics, Shanxi University of Finance & Economics, Taiyuan 030006, China;
    2 School of Management Science and Engineering, Shanxi University of Finance & Economics, Taiyuan 030006, China
  • Received:2013-09-10 Revised:2014-08-11 Online:2015-01-18 Published:2015-01-18
  • Contact: ZHAO Guohao
  • Supported by:

    National Natural Science Foundation in China (No. 71173141), development projects in Higher Education Institution of Shanxi Province of China (No.20111312), special funds projects in Higher Education Institution of Shanxi Province of China (No.201246), National Natural Science Foundation in China (No.71373170), and soft science research project in Shanxi Province of China (No.2013041015-04).

Abstract:

Research on coke price forecasting is of theoretical and practical significance. Here, the Kalman filtering algorithm was used to analyze the price of coke. As the only state variable, the historical coke price is sorted out to build the state space model. The algorithm makes use of innovation composed of the difference between observed and predicted values, and allows us to obtain the optimal estimated value of the coke price via continuous updating and iteration of innovation. Our results show that this algorithm is effective in the field of coke price tracking and forecasting.

Key words: state space model, forecasting, Kalman filtering algorithm, coke price