Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks

Abstract Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scal...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Pramit Pandit, Atish Sagar, Bikramjeet Ghose, Moumita Paul, Ozgur Kisi, Dinesh Kumar Vishwakarma, Lamjed Mansour, Krishna Kumar Yadav
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Nature Portfolio 2024-11-01
Sarja:	Scientific Reports
Aiheet:	Agriculture price forecasting Empirical mode decomposition Intrinsic mode functions Non-linearity Time delay neural network
Linkit:	https://doi.org/10.1038/s41598-024-74503-4
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