Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Fuzzy neural networks with application to sales forecasting
Fuzzy Sets and Systems
The Benefits of Advance Booking Discount Programs: Model and Analysis
Management Science
Suitability of different neural networks in daily flow forecasting
Applied Soft Computing
Online option price forecasting by using unscented Kalman filters and support vector machines
Expert Systems with Applications: An International Journal
Modular neural networks for recursive collaborative forecasting in the service chain
Knowledge-Based Systems
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Artificial intelligence diagnosis algorithm for expanding a precision expert forecasting system
Expert Systems with Applications: An International Journal
Designing a decision-support system for new product sales forecasting
Expert Systems with Applications: An International Journal
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
Learning capability and storage capacity of two-hidden-layer feedforward networks
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
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In the fashion retail industry, a versatile sales forecasting system is crucial for fashion retailers. In order to avoid stock-out and maintain a high inventory fill rate, fashion retailers require specific and accurate sales forecasting systems. In this study, a hybrid method based on extreme learning machine model with the adaptive metrics of inputs is proposed for improving sales forecasting accuracy. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and reduce the effect of the overfitting of networks. The proposed algorithms are validated using real POS data of three fashion retailers selling high-ended, medium and basic fashion items in Hong Kong. It was found that the proposed model is practical for fashion retail sales forecasting and outperforms the auto-regression (AR), artificial neural network (ANN), and extreme learning machine (ELM) models.