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

• Published in: PloS one Vol. 19; no. 7; p. e0304881
• Main Authors: Zhang, Qi, Yang, Weijia, Zhao, Anping, Wang, Xiaodong, Wang, Zengfei, Zhang, Lin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.07.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Agricultural production; Beijing; Biology and Life Sciences; Commerce - economics; Commerce - trends; Computer and Information Sciences; Datasets; Decision making; Deep learning; Economic forecasting; Forecast accuracy; Forecasting; Forecasts and trends; Humans; Industrial development; Learning algorithms; Machine Learning; Market prices; Models, Economic; Neural networks; Performance prediction; Physical Sciences; Prices and rates; Pricing; Research and Analysis Methods; Stability augmentation; Statistical analysis; Stochastic models; Supply & demand; Supply chains; Support vector machines; Sustainable development; Time series; Vegetable industry; Vegetables; Vegetables - economics; Vegetables - growth & development; Wavelet transforms; Beijing; China; Beijing China

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 influe...