Neural computing: theory and practice
Neural computing: theory and practice
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Neural networks and the bias/variance dilemma
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy neural networks with application to sales forecasting
Fuzzy Sets and Systems
Artificial Neural Networks: Approximation and Learning Theory
Artificial Neural Networks: Approximation and Learning Theory
Decision Support Systems - Special issue: Data mining for financial decision making
Agent-based demand forecast in multi-echelon supply chain
Decision Support Systems
A hybrid sales forecasting system based on clustering and decision trees
Decision Support Systems
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry
Expert Systems with Applications: An International Journal
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Capabilities of a four-layered feedforward neural network: four layers versus three
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Applying text and data mining techniques to forecasting the trend of petitions filed to e-People
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A two-stage dynamic sales forecasting model for the fashion retail
Expert Systems with Applications: An International Journal
Intelligent fabric hand prediction system with fuzzy neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An intelligent fast sales forecasting model for fashion products
Expert Systems with Applications: An International Journal
Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs
Knowledge-Based Systems
Robust extreme learning machine
Neurocomputing
Computers and Industrial Engineering
Displacement prediction model of landslide based on ensemble of extreme learning machine
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
A multivariate intelligent decision-making model for retail sales forecasting
Decision Support Systems
A study on the randomness reduction effect of extreme learning machine with ridge regression
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Expert Systems with Applications: An International Journal
Looking for representative fit models for apparel sizing
Decision Support Systems
Long-term time series prediction using OP-ELM
Neural Networks
Meta-ELM: ELM with ELM hidden nodes
Neurocomputing
Application of BW-ELM model on traffic sign recognition
Neurocomputing
Fast fashion sales forecasting with limited data and time
Decision Support Systems
Hi-index | 0.01 |
Sales forecasting is a challenging problem owing to the volatility of demand which depends on many factors. This is especially prominent in fashion retailing where a versatile sales forecasting system is crucial. This study applies a novel neural network technique called extreme learning machine (ELM) to investigate the relationship between sales amount and some significant factors which affect demand (such as design factors). Performances of our models are evaluated by using real data from a fashion retailer in Hong Kong. The experimental results demonstrate that our proposed methods outperform several sales forecasting methods which are based on backpropagation neural networks.