The Price Prediction of Vegetables by Using Machine Learning

Abstract

Vegetable prices are great important in ensuring people's livelihood. With the progress of science and technology, machine learning algorithms have been applied in the field of vegetable price prediction. This paper analyzed the model that has been used in vegetable price prediction and its results and compared the accuracy of each model. The study in this paper concluded that LSTM is suitable for handling sequence data with long-term dependence but this model requires a large amount of data for training. ARIMA is suitable for short-term predictions but may not fit well with non-stationary and nonlinear data. In addition, this paper introduces other algorithms. For example, the paper shows How to use Lasso regression to compress the input variables and how to reduce model complexity and improve model generalization ability. The BP neural network was used to fit the output variables to improve the model fitting ability. This paper further studies and summarizes the combination of each model and the accuracy of the combined model for predicting vegetable prices. It can be seen that the prediction error of the L-LBNN model is very small, which can be effectively used in the price prediction of vegetables. The purpose of this study is to summarize the current situation of predicting vegetable prices with machine learning algorithms to provide guidance and inspiration for researchers and to promote the development of the use of machine learning in the field of price prediction of vegetables.