Journal of Resources and Ecology ›› 2018, Vol. 9 ›› Issue (3): 302-305.doi: 10.5814/j.issn.1674-764x.2018.03.010

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Forecasting the Ethiopian Coffee Price Using Kalman Filtering Algorithm

Tesfahun Berhane1, Nurilign Shibabaw1, Aemiro Shibabaw1, Molalign Adam1, Abera A. Muhamed2   

  1. 1. Department of mathematics, Bahir Dar University, Bahir Dar, P.O.Box 3018, Ethiopia;
    2. Department of mathematics, Kotebe Metropolitan University, Addis Ababa, P.O.Box 31248, Ethiopia
  • Received:2017-10-09 Revised:2018-01-20 Online:2018-05-30 Published:2018-06-06
  • Contact: Tesfahun Berhane, E-mail: tesfahunb2002@gmail.com.

Abstract: Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical importance.This study aims at forecasting the coffee price in Ethiopia. We used daily closed price data of Ethiopian coffee recorded in the period 25 June 2008 to 5 January 2017 obtained from Ethiopia commodity exchange (ECX) market to analyse coffee prices fluctuation. Here, the nature of coffee price is non-stationary and we apply the Kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of coffee price. The performance of the algorithm for estimating and forecasting the coffee price is evaluated by using root mean square error (RMSE). Based on the linear state space model and the Kalman filtering algorithm, the root mean square error (RMSE) is 0.000016375, which is small enough, and it indicates that the algorithm performs well.

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