Improved supply chain management based on hybrid demand forecasts

By Luis Aburto, Richard Weber. Applied Soft Computing, 2007

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A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile (2008, methodology): “…has recently been used for tourist arrival forecasting (Aslanargun et al., 2007), hydrology (Jain and Kumar, 2007), supply chain management (Aburto and Weber, 2007), freshwater phytoplankton dynamics (Jeong et al., 2008), watersheds (Pulido-Calvo and Portela, 2007), ﬁsh catch…”
Performance of state space and ARIMA models for consumer retail sales forecasting (2015, methodology): “Motivated by the particular advantages of ARIMA models and ANNs, Aburto and Weber [11] developed a hybrid intelligent system combining ARIMA type approaches and MLP-type neural networks for demand forecasting that showed improvements in forecasting accuracy.”
Retail forecasting: Research and practice (2019, methodology): “The models used include Back Propagation Neural Networks (Aburto and Weber, 2007; Ainscough and Aronson, 1999), Fuzzy Neural Networks (Kuo, 2001); Regression Trees (Gür Ali et al.”
Application of type-2 neuro-fuzzy modeling in stock price prediction (2012, methodology): “Then the antecedent and consequent parameters associated with the rules are refined by particle swarm optimization and least squares eywords: tock forecasting ype-2 fuzzy set SK rule elf-constructing fuzzy clustering article swarm optimization east squares estimation estimation.”