Forecasting Horticultural Products Price Using ARIMA Model and Neural Network Based on a Large-Scale Data Set Collected by Web Crawler

Abstract

The sales of agricultural products are an important component of the product supply chain. The price of agricultural products, a social signal of product supply and demand, is affected by many factors, such as climate, price, policy, and so on. Due to the asymmetry between production and marketing information, the price of many agricultural products fluctuates greatly. Horticultural products are especially sensitive to price since they are not suitable for long-term storage. Therefore, forecasting the price of horticultural products is very helpful in designing a cropping plan. In this paper, AutoRegressive Integrated Moving Average (ARIMA) model, back propagation (BP) network method, and recurrent neural network (RNN) method were tested to forecast the price of agricultural products (cucumber, tomato, and eggplant) in short term (several days) and long term (several weeks or months). A large-scale price data of agricultural products were collected from the website based on web crawler technology. Since ARIMA requires continuous and periodic data, it is suitable for small-scale periodic data. It gave good performance for average monthly data but not for daily data. Instead, the neural network methods (including BP network and RNN) can predict well daily, weekly, and monthly trend of price fluctuation. It is more suitable for large-scale data. It is expected that the deep learning method represented by a neural network will become the mainstream method of agricultural product price forecasting.