A Data Analysis and Market Price Prediction of Ethiopian Commodity Market with Machine Learning Algorithms

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The current Ethiopian market is conducted in a traditional manner and market drivers are still not used for prediction of future market price. Although, large amount of market data have been gathered throughout years by both governmental and non-governmental organizations, yet little have been done to analyze the data for future market price prediction. Moreover, the analysis methods were often manual creating inefficiency in time and quality of market prediction. Analyzing valuable data will show us what the future holds and accelerate the development goals of the country in the sector. The study examines features of current Ethiopian market attributes to find out most valuable features for predicting market price. Eighteen technical indicators are taken and tested for their individual ability of prediction and redundancy. From the feature selection of commodity marke, we have found that features like Stochastic %K, Stochastic %D, Close gain/loss, High, close price, Opening Price, Low, RSI, Ton and Moving Average Convergence/ divergence (MACD) founded to be in the top ten of individual performance evaluation. Moreover features namely Stochastic %K, Relative Strength Index (RSI), Bollinger Bands-Upper, Highest-High, close gain/loss, Simple Moving Average (SMA), Closing price, MACD-Fast, Exponential Moving Average (EMA), MACD-Slow and Low founded to be less redundant. The study also compares four machine learning models for their prediction ability of Ethiopian commodity market price. The outcomes of feature selection were used to compare the models. Two experiments were conducted; the first was comparison of the models with 10 fold cross validation using feature of high individual predictive ability and less redundancy. The second one was a comparison of models with separate train and test data using features of high individual predictive ability and less redundancy. From the models (Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (K-NN) and Ensemble Learning) the performance of ANN and Ensemble Learning algorithms are shown to be accurate than SVM and K-NN. The average MAE rate of the ANN model was 2.8084. Ensemble Learning and SVM follow with average MAE rate of 4.9362 and 8.1178 respectively. The other model was least performer with the MAE rate above 45.3381.



Feature selection, Technical Indicators Price prediction, Machine Learning Algorithms