Forecasting Ethiopian Agricultural Commodity Price Using Time Series Features and Technical Indicators

Agricultural commodity price prediction helps the government, investors, and farmers to make informed decisions. Realizing the benefit, several researchers proposed different prediction models that use different features. However, most prediction models are affected by factors, such as data type (e.g., linear and nonlinear), seasonality of commodity items, weather conditions, commodity volatility features, and country economic factors. To solve this problem, we propose a model that combines time-series features and technical indicators to forecast the commodity price. The prediction model is created with four machine-learning algorithms: artificial neural network (ANN), support vector machine (SVM), random forest, and MLP regressor. The prediction model is built using four-machine learning algorithms namely, artificial neural network (ANN), support vector machine (SVM), random forest, and MLP regressor. To see the impact of combined features, we conducted two experiments using coffee and sesame datasets. The performance of the prediction models is assessed using the root mean square error (RMSE) and mean average error (MAE). The results show that the proposed model perform better than the baseline approach by an average by an average of 4.3753, 4.4216, 2.7494, and 6.658 while using Artificial Neural Networks, Support Vector Machines, MLP regressor, and Random Forest, respectively. To see which of the features contributed to the improvement of agricultural commodity price prediction, we computed feature importance using ReliefFAttributeEval. The result shows that: EMA, DEMA, SMA, True High, True Low, RSI, Trend, ADX, Seasonality (volatility), and CMO were founded in the top 10 in their predictive ability with the respective order.