Forecasting world sugar contract futures using long short-term memory technique with multi-step ahead forecasting strategy

Abstract

Time series analysis using stochastic and dynamic models for data forecasting is a key in assisting planning and decision-making processes in various sectors. Long short-term memory (LSTM), with its advantage in understanding patterns and non-linearity in sequential data, is applied in a multi-step ahead forecasting strategy on world sugar futures prices. Fluctuations in sugar prices have a significant impact on the agriculture, trade, and food industry sectors. Forecasting sugar prices becomes a crucial tool for industries, investors, and traders to anticipate changes and make informed decisions. The objectives of this study are to identify the best strategy for forecasting the world sugar contract price and to perform forecasting using the best model. The research results indicate that hyperparameter tuning in LSTM models produces varied combinations and effects. Furthermore, the recursive strategy is suitable for long-term forecasting, while the direct strategy is appropriate for short-term forecasting. Forecasting values for long-term periods remains challenging in achieving high accuracy.