Predictive Model for ECX Coffee Contracts
| dc.contributor.advisor | Hailemariam, Sebsibe(PhD) | |
| dc.contributor.author | Mulugeta, Frehiwot | |
| dc.date.accessioned | 2018-06-19T15:01:54Z | |
| dc.date.accessioned | 2023-11-29T04:07:10Z | |
| dc.date.available | 2018-06-19T15:01:54Z | |
| dc.date.available | 2023-11-29T04:07:10Z | |
| dc.date.issued | 2014-10 | |
| dc.description.abstract | Ethiopia Commodity Exchange is a commodity market that transforms the traditional agricultural marketing system into modern and transparent market. Ethiopia is known for its high quality and highly diversified type of coffee and ECX has designed detailed coffee contract and the market executes many trades for these contracts. This research aims to study the relationship between ECX coffee contract and to propose prediction model that assists the market to undertake efficient coffee trading system. The price prediction model will be used to predict the daily selling price of all coffee contracts. The prediction model was developed by the most widely used machine learning method, Artificial Neural Network. Five and half years of ECX coffee trading data have been used to analyze the problem, to train and test the models. The coffee trading data have been studied intensively by correlation coefficient and scatter plot matrixes using volume of coffee traded in the market and availability of the contract in a year. It was found that washed Sidama coffee A grade 3 (WSDA3) contract was traded in a larger volume and available throughout the year. Moreover, the contract is highly correlated with most coffee contracts. And thus WSDA3 was selected as a reference contract to represent all export coffee contracts. Coffee contracts daily price data show non-linear characteristics. Traditional statistical methods are unable to develop prediction model for non-linear data. Artificial neural network can flexibly model linear or non-linear relationship between variables. Among the artificial network algorithms; the radial basis function neural network (RBF) and multilayer perceptron neural network (MLP) are used to approximate any linear or non-linear function. MLP and RBF methods were employed to develop coffee contracts price prediction model. Three experiments were designed to build the coffee contract price prediction models. For washed Sidama coffee, for unwashed Sidama coffee contracts and for contracts different from Sidama origin. The performance of the models was evaluated on the test data set by coefficient of determination and mean squared error. The experimental result reveals that large R2 values with small variance were obtained in MLP based models than RBF Based models. Moreover, the smallest MSE with small variance is observed in MLP based models as compared to models constructed by RBF algorithm. The ix results obtained from the study showed that the MLP networks are capable of predicting the daily price of coffee contracts than the RBF networks because MLP networks are global function approximators. In MLP base models, the largest R2 with smallest variance is achieved in Sidama washed coffee and different origin washed coffee contracts. Similarly, MLP based models the smallest MSE with minimum variance is achieved Sidama washed coffee and different origin washed coffee. Sidama washed coffee and different origin washed coffee contracts respectively. The accuracy results of washed coffee contracts using MLP algorithms are higher than unwashed contracts. Generally, coffee contract that belong to the same processing type to the reference contract (WSDA3) has higher accuracy result than that of contract in different processing type. Key words: ANN, Coffee Contract, ECX, MLP, Machine Learning, RBF, Price Prediction | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/1864 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Ann; Coffee Contract; Ecx; Mlp; Machine Learning, Rbf; Price Prediction | en_US |
| dc.title | Predictive Model for ECX Coffee Contracts | en_US |
| dc.type | Thesis | en_US |