An introduction to wavelets
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
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
Improved supply chain management based on hybrid demand forecasts
Applied Soft Computing
Evolving neural network for printed circuit board sales forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mean–Variance Analysis for the Newsvendor Problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiresolution forecasting for futures trading using wavelet decompositions
IEEE Transactions on Neural Networks
An empirical study of intelligent expert systems on forecasting of fashion color trend
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Fast fashion sales forecasting with limited data and time
Decision Support Systems
Fuzzy artificial neural network p, d, q model for incomplete financial time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Time series forecasting, as an important tool in many decision support systems, has been extensively studied and applied for sales forecasting over the past few decades. There are many well-established and widely-adopted forecasting methods such as linear extrapolation and SARIMA. However, their performance is far from perfect and it is especially true when the sales pattern is highly volatile. In this paper, we propose a hybrid forecasting scheme which combines the classic SARIMA method and wavelet transform (SW). We compare the performance of SW with (i) pure SARIMA, (ii) a forecasting scheme based on linear extrapolation with seasonal adjustment (CSD+LESA), and (iii) evolutionary neural networks (ENN). We illustrate the significance of SW and establish the conditions that SW outperforms pure SARIMA and CSD+LESA. We further study the time series features which influence the forecasting accuracy, and we propose a method for conducting sales forecasting based on the features of the given sales time series. Experiments are conducted by using real sales data, hypothetical data, and publicly available data sets. We believe that the proposed hybrid method is highly applicable for forecasting sales in the industry.