Forecasting the Ethiopian Coffee Price Using Kalman Filtering Algorithm

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.     

用卡尔曼滤波算法预测埃塞俄比亚咖啡价格（英文）

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。     

Spatio-temporal Rainfall Distribution and Markov Chain Analogue Year Stochastic Daily Rainfall Model in Ethiopia

This paper aims at the spatiotemporal distribution of rainfall in Ethiopia and developing stochastic daily rainfall model. Particularly, in this study, we used a Markov Chain Analogue Year (MCAY) model that is, Markov Chain with Analogue year (AY) component is used to model the occurrence process of daily rainfall and the intensity or amount of rainfall on wet days is described using Weibull, Log normal, mixed exponential and Gamma distributions. The MCAY model best describes the occurrence process of daily rainfall, this is due to the AY component included in the MC to model the frequency of daily rainfall. Then, by combining the occurrence process model and amount process model, we developed Markov Chain Analogue Year Weibull model (MCAYWBM), Markov Chain Analogue Year Log normal model (MCAYLNM), Markov Chain Analogue Year mixed exponential model (MCAYMEM) and Markov Chain Analogue Year gamma model (MCAYGM). The performance of the models is assessed by taking daily rainfall data from 21 weather stations (ranging from 1 January 1984-31 December 2018). The data is obtained from Ethiopia National Meteorology Agency (ENMA). The result shows that MCAYWBM, MCAYMEM and MCAYGM performs very well in the simulation of daily rainfall process in Ethiopia and their performances are nearly the same with a slight difference between them compared to MCAYLNM. The mean absolute percentage error (MAPE) in the four models: MCAYGM, MCAYWBM, MAYMEM and MCAYLNM are 2.16%, 2.27%, 2.25% and 11.41% respectively. Hence, MCAYGM, MCAYWBM, MAYMEM models have shown an excellent performance compared to MCAYLNM. In general, the light tailed distributions: Weibull, gamma and mixed exponential distributions are appropriate probability distributions to model the intensity of daily rainfall in Ethiopia especially, when these distributions are combined with MCAYM.