Deep Learning Based Approach for Fresh Produce Market Price Prediction

Abstract

Building highly precise prediction models for Fresh Produce (FP) market price is crucial to protect retailers from overpriced FP. In this paper we are comparing the price prediction models performance of deep learning (DL) models with statistical as well as standard machine learning (ML) models. Five types of FP are considered in performance testing. It is found that the conventional ML models outperform the statistical models such as ARIMA. On the other hand, the winning model among the conventional ML models (the Gradient Boosting model) proves to be less performant as compared with the simple or compound DL models. Moreover, the simple DL models, such as the Long Short-Term Memory (LSTM), are outperformed by the compound one, the Convolutional Long Short-Term Memory Recurrent Neural Network (CNN-LSTM), whose performance improves by adding attention. The model is capable of precisely predicting FP prices for up to three weeks ahead.