Forecasting sesame price using Kalman filter algorithm

Abstract

This study aims at forecasting the white Humera Gondar sesame class 4 (WHGS4) price in Ethiopia. We used the daily closed price data of Ethiopian sesame recorded in the period 2 January 2012 to 30 March 2018 obtained from Ethiopia commodity exchange (ECX) to analyse the price of sesame. We applied the Kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of sesame price. We used root mean square error (RMSE) to evaluate the performance of the algorithm for estimating and forecasting the sesame price . Based on the linear state space model and the Kalman filtering algorithm, the root mean square error (RMSE) is 0.000001877 , which is small enough, and it indicates that the algorithm performs well.