Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

Abstract Background Grain commodities are important to people's daily lives and their fluctuations can cause instability for households. Accurate prediction of grain prices can improve food and social security. Methods & Materials This study proposes a hybrid Long Short‐Term Memory (LSTM)‐C...

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Bibliographic Details
Main Authors: Zilin Wang, Niamh French, Thomas James, Calogero Schillaci, Faith Chan, Meili Feng, Aldo Lipani
Format: Article
Language:English
Published: Wiley 2023-09-01
Series:Journal of Sustainable Agriculture and Environment
Subjects:
commodity prices
LSTM‐CNN
multistep ahead prediction
snow water equivalent
Online Access:https://doi.org/10.1002/sae2.12041

Internet

https://doi.org/10.1002/sae2.12041

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