Forecasting sesame price using Kalman filter algorithm

Tesfahun Berhane, Molalign Adam, Nurilign Shibabaw, Aemiro Shibabaw, Abera A. Muhamed

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.

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Published: 2019-06-25

How to Cite this Article:

Tesfahun Berhane, Molalign Adam, Nurilign Shibabaw, Aemiro Shibabaw, Abera A. Muhamed, Forecasting sesame price using Kalman filter algorithm, Math. Finance Lett., 2019 (2019), Article ID 3

Copyright © 2019 Tesfahun Berhane, Molalign Adam, Nurilign Shibabaw, Aemiro Shibabaw, Abera A. Muhamed. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Mathematical Finance Letters

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