  The intention of this research is to study and design an automated
agriculture commodity price prediction system with novel machine learning
techniques. Due to the increasing large amounts historical data of agricultural
commodity prices and the need of performing accurate prediction of price
fluctuations, the solution has largely shifted from statistical methods to
machine learning area. However, the selection of proper set from historical
data for forecasting still has limited consideration. On the other hand, when
implementing machine learning techniques, finding a suitable model with optimal
parameters for global solution, nonlinearity and avoiding curse of
dimensionality are still biggest challenges, therefore machine learning
strategies study are needed. In this research, we propose a web-based automated
system to predict agriculture commodity price. In the two series experiments,
five popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM
have been compared with large historical datasets in Malaysia and the most
optimal algorithm, LSTM model with an average of 0.304 mean-square error has
been selected as the prediction engine of the proposed system.
