AFTER-IQEA Combination Forecasting Model for Cosmetics Sales Forecasting
Cosmetics is necessary for everyones daily live, its impact on economy can not be ignored, but severe inventory stacking and lacking problems still exist. However, the occurrence of these problems is likely to be decreased via forecasting demand accurately. Thus, an Aggregated Forecast Through Exponential Re-weighting-Improved Quantum Evolutionary Algorithm (AFTER-IQEA) forecasting model is developed in this research. Important influential factors are chosen by Gray Relation Analysis, while considering the seasonal factor by Winters exponential smoothing. Three models: Evolving Neural Network (ENN), Adaptive Network-based Fuzzy Inference System (ANFIS), and Particle Swarm Optimization Wavelet v-Support Vector Machine (PSOWv-SVM) are used to forecast separately, and then integrate into AFTER-IQEA by dynamic weights generated from AFTER algorithm. The effectiveness of the proposed approach is demonstrated using real-world data, and it is superior to other traditional statistical models and neural network.
Combination forecasting Exponential Reweighting Quantum Evolutionary Algorithm Gray Relation Analysis Winters exponential smoothing.
Wu Di Li Haitao Li Sujian Liu Bo
Department of Logistics Engineering University of Science and Technology Beijing Beijing, China Department of Logistics Engineering, Professor University of Science and Technology Beijing Beijing,
国际会议
北京
英文
75-78
2010-08-08（万方平台首次上网日期，不代表论文的发表时间）