Forecasting Flower Prices by Long Short-Term Memory Model with Optuna

Abstract

The oriental lily ‘Casa Blanca’ is one of the most popular and high-value flowers. The period for keeping these flowers refrigerated is limited. Therefore, forecasting the prices of oriental lilies is crucial for determining the optimal planting time and, consequently, the profits earned by flower growers. Traditionally, the prediction of oriental lily prices has primarily relied on the experience and domain knowledge of farmers, lacking systematic analysis. This study aims to predict daily oriental lily prices at wholesale markets in Taiwan using many-to-many Long Short-Term Memory (MMLSTM) models. The determination of hyperparameters in MMLSTM models significantly influences their forecasting performance. This study employs Optuna, a hyperparameter optimization technique specifically designed for machine learning models, to select the hyperparameters of MMLSTM models. Various modeling datasets and forecasting time windows are used to evaluate the performance of the designed many-to-many Long Short-Term Memory with Optuna (MMLSTMOPT) models in predicting daily oriental lily prices. Numerical results indicate that the developed MMLSTMOPT model achieves highly satisfactory forecasting accuracy with an average mean absolute percentage error value of 12.7%. Thus, the MMLSTMOPT model is a feasible and promising alternative for forecasting the daily oriental lily prices.