التفاصيل البيبلوغرافية
| العنوان: |
Forecasting the Ethiopian Coffee Price Using Kalman Filtering Algorithm. |
| المؤلفون: |
Berhane, Tesfahun, Shibabaw, Nurilign, Shibabaw, Aemiro, Adam, Molalign, Muhamed, Abera A. |
| المصدر: |
Journal of Resources & Ecology; 2018 Special Issue, Vol. 9, p302-305, 4p |
| مصطلحات موضوعية: |
COFFEE sales & prices, KALMAN filtering |
| مصطلحات جغرافية: |
ETHIOPIA |
| Abstract (English): |
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. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
埃塞俄比亚咖啡价格波动很大，因此对国家经济发展的影响不容小视，对咖啡价格进行预测具有理论和实践意义。 为了分析咖啡价格波动，我们采用来自埃塞俄比亚商品交易所（ECX）记录的2008 年6 月25 日至2017 年1 月5 日期间咖啡日 收盘价数据。在这里，咖啡价格的性质是非平稳的，我们在单个线性状态空间模型上应用卡尔曼滤波算法来预测咖啡价格的最优 值，主要通过使用均方根误差（RMSE）来评估用于预测咖啡价格的算法的性能。基于线性状态空间模型和卡尔曼滤波算法，均 方根误差（RMSE）为0.000016375，说明该算法性能良好，研究结果可靠. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
