Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques

• URL: http://arxiv.org/abs/2106.12747v1
• Date: Thu, 24 Jun 2021 03:10:25 GMT
• Title: Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques
• Authors: Zhiyuan Chen, Howe Seng Goh, Kai Ling Sin, Kelly Lim, Nicole Ka Hei Chung and Xin Yu Liew
• Abstract summary: We propose a web-based automated system to predict agriculture commodity price. The most optimal algorithm, LSTM model with an average of 0.304 mean-square error has been selected as the prediction engine.
• Score: 0.8998318101090188
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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