A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

Accurate and real-time product demand forecasting is the need of the hour in the world of supply chain management. Predicting future product demand from historical sales data is a highly non-linear problem, subject to various external and environmental factors. In this work, we propose an optimised...

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Vydáno v:	Applied intelligence (Dordrecht, Netherlands) Ročník 52; číslo 10; s. 11489 - 11505
Hlavní autoři:	Chaudhuri, Koushiki Dasgupta, Alkan, Bugra
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.08.2022 Springer Nature B.V
Témata:	Accuracy Algorithms Artificial Intelligence Artificial neural networks Autoregressive models Computer Science Demand Economic forecasting Forecasting Machine learning Machines Manufacturing Mechanical Engineering Neural networks Nodes Optimization Performance measurement Processes Real time Recurrent neural networks Root-mean-square errors Sales Statistical analysis Supply chains Tuning Supply chain management Harris hawks optimisation Extreme learning machines Optimisation Demand forecasting ARIMA Artificial neural networks Hyperparameter tuning
ISSN:	0924-669X, 1573-7497
On-line přístup:	Získat plný text
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