Deep learning-based prediction model for crude palm oil prices using news sentiment analysis with sliding window

Abstract

Crude palm oil (CPO) price prediction plays an important role in agricultural economic development. Various economics and agricultural-related factors have been used to predict CPO prices. Nevertheless, understanding news sentiment features will also be important in CPO price prediction. This paper proposes a CPO price prediction model to help the plantation organizations in the palm oil sector to successfully anticipate CPO price fluctuations and manage the resources more effectively. The CPO price behavior is nonlinear in nature, and thus prediction is very difficult. In this paper, an improved version of recurrent network, long short-term memory (LSTM)-based CPO price prediction model with news sentiment, is used to produce an enhanced prediction model. The findings of this study show that the LSTM-based forecasting model with news headline sentiment using a six-month sliding window produced the best result in forecasting the CPO price movement compared to other sliding window sizes