Short-term forecasting of vegetable prices based on LSTM model-Evidence from Beijing's vegetable data.

ABSTRACT: The vegetable sector is a vital pillar of society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. In this paper, we exploit the average daily price data of six distinct types of vegetables sourced from seven key wholesale markets in Beijing, spanning from 2009 to 2023. Upon training an LSTM model, we discovered that it exhibited exceptional performance on the test dataset. Demonstrating robust predictive performance across various vegetable categories, the LSTM model shows commendable

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC11239047 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature