Introduction to Grey system theory
The Journal of Grey System
The integration and application of fuzzy and grey modeling methods
Fuzzy Sets and Systems
Quick response in manufacturer-retailer channels
Management Science - Special issue on frontier research in manufacturing and logistics
Fuzzy neural networks with application to sales forecasting
Fuzzy Sets and Systems
A bibliography of neural network business applications research: 1994–1998
Computers and Operations Research - Neural networks in business
The Benefits of Advance Booking Discount Programs: Model and Analysis
Management Science
Deterministic fuzzy time series model for forecasting enrollments
Computers & Mathematics with Applications
Forecasting analysis by using fuzzy grey regression model for solving limited time series data
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Modular neural networks for recursive collaborative forecasting in the service chain
Knowledge-Based Systems
Grey system theory-based models in time series prediction
Expert Systems with Applications: An International Journal
Prediction of multivariate chaotic time series with local polynomial fitting
Computers & Mathematics with Applications
Integrate the GM(1,1) and Verhulst models to predict software stage effort
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive time-variant models for fuzzy-time-series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ratio-based lengths of intervals to improve fuzzy time series forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers & Mathematics with Applications
Empirical models based on features ranking techniques for corporate financial distress prediction
Computers & Mathematics with Applications
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In the fashion retail industry, level of forecasting accuracy plays a crucial role in retailers' profit. In order to avoid stock-out and maintain a high inventory fill rate, fashion retailers require specific and accurate sales forecasting systems. One of the key factors of an effective forecasting system is the availability of long and comprehensive historical data. However, in the fashion retail industry, the sales data stored in the point-of-sales (POS) systems are always not comprehensive and scattered due to various reasons. This paper presents a new seasonal discrete grey forecasting model based on cycle truncation accumulation with amendable items to improve sales forecasting accuracy. The proposed forecasting model is to overcome two important problems: seasonality and limited data. Although there are several works suitable with one of them, there is no previous research effort that overcome both problems in the context of grey models. The proposed algorithms are validated using real POS data of three fashion retailers selling high-ended, medium and basic fashion items. It was found that the proposed model is practical for fashion retail sales forecasting with short historical data and outperforms other state-of-art forecasting techniques.