Forecasting Ethiopian Agricultural Commodity Price Using Time Series Features and Technical Indicators
| dc.contributor.advisor | Surafel, Lemma (PhD) | |
| dc.contributor.author | Sisay, Gebremedhin | |
| dc.date.accessioned | 2022-05-23T08:30:02Z | |
| dc.date.accessioned | 2023-11-04T15:14:45Z | |
| dc.date.available | 2022-05-23T08:30:02Z | |
| dc.date.available | 2023-11-04T15:14:45Z | |
| dc.date.issued | 2022-05 | |
| dc.description.abstract | 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. Among these factors, the most significant impediments to the accuracy of commodity price prediction are seasonality and trend pattern. To fill this gap, we propose a model that predicts commodity prices through the combination of time series features and technical indicators. The prediction model is built using four-machine learning algorithms: Artificial Neural Network, Extreme Learning Machine, Support Vector Machine, and Random Forest. To assess the impact of the proposed approach, 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 approach improves agricultural commodity price prediction performance in all cases except MAE of sesame while using Extreme Learning Machine. Using Artificial Neural Network, Extreme Learning Machine, Support Vector Machine and Random Forest, the RMSE of price prediction is reduced by an average of 4.37, 4.42, 2.74, and 5.15, respectively. Finally, among the four machine learning algorithms used in the study, Artificial Neural Network is found to be the best algorithm for enhancing the performance of agricultural commodity price prediction. We also conclude from our experiment result that considering commodity properties such as periodicity, volatility, linearity, momentum, volume, and trend would improve the performance of agricultural commodity price prediction. To see which of the features contributed more to the improvement of agricultural commodity price prediction, we computed feature importance using Random forest algorithms. The result shows that: close, high, low, open, exponential moving average (EMA), double exponential moving average (DEMA), simple moving average (SMA), truehigh, truelow, trend, seasonality, relative strength index (RSI) are the most important features in sesame and coffee price prediction. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/31732 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | technical indicator | en_US |
| dc.subject | time series feature | en_US |
| dc.subject | price forecasting | en_US |
| dc.subject | agricultural commodity | en_US |
| dc.title | Forecasting Ethiopian Agricultural Commodity Price Using Time Series Features and Technical Indicators | en_US |
| dc.type | Thesis | en_US |