SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization for Agricultural Time-Series Prediction

SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization for Agricultural Time-Series Prediction

In the domain of agricultural product sales and consumption forecasting, the presence of infrequent yet impactful events such as livestock epidemics and mass media influences poses substantial challenges. These rare occurrences, termed Sparse Critical Events (SCEs), often lead to predictions converg...

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Bibliographic Details
Main Authors: Ga-Ae Ryu, Tserenpurev Chuluunsaikhan, Aziz Nasridinov, HyungChul Rah, Kwan-Hee Yoo
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Agriculture
Subjects:
sparse critical event-driven LSTM (SCE-LSTM)
forecasting
pork consumption
unstructured big data
Online Access:https://www.mdpi.com/2077-0472/13/11/2044

Internet

https://www.mdpi.com/2077-0472/13/11/2044

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