Exploring the use of deep neural networks for sales forecasting in fashion retail
Tài liệu tham khảo
Agrawal, 1996, Market share forecasting: an empirical comparison of artificial neural networks and multinomial logit model, Journal of Retailing, 72, 383, 10.1016/S0022-4359(96)90020-2
Baecke, 2017, Investigating the added value of integrating human judgement into statistical demand forecasting systems, International Journal of Production Economics, 191, 85, 10.1016/j.ijpe.2017.05.016
Sodero, 2017, Demand and revenue management of deteriorating inventory on the internet: an empirical study of flash sales markets, Journal of Business Logistics, 38, 170, 10.1111/jbl.12157
Sun, 2008, Sales forecasting using extreme learning machine with applications in fashion retailing, Decision Support Systems, 46, 411, 10.1016/j.dss.2008.07.009
Xia, 2014, A seasonal discrete grey forecasting model for fashion retailing, Knowledge-Based Systems, 57, 119, 10.1016/j.knosys.2013.12.014
Corsten, 2004, Stock-outs cause walkouts, Harvard Business Review, 82, 26
Lee, 1997, Information distortion in a supply chain: the bullwhip effect, Management Science, 43, 546, 10.1287/mnsc.43.4.546
Huang, 2017, Intelligent retail forecasting system for new clothing products considering stock-out, Fibres & Textiles in Eastern Europe, 25, 10, 10.5604/01.3001.0010.1704
Allenby, 1996, Economic trends and being trendy: the influence of consumer confidence on retail fashion sales, Journal of Business & Economic Statistics, 14, 103
Choi, 2014, Fast fashion sales forecasting with limited data and time, Decision Support Systems, 59, 84, 10.1016/j.dss.2013.10.008
Choi, 2012, Color trend forecasting of fashionable products with very few historical data, IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 42, 1003, 10.1109/TSMCC.2011.2176725
Liu, 2013, Sales forecasting for fashion retailing service industry: a review, Mathematical Problems in Engineering
Ni, 2011, A two-stage dynamic sales forecasting model for the fashion retail, Expert Systems with Applications, 38, 1529, 10.1016/j.eswa.2010.07.065
Thomassey, 2006, A hybrid sales forecasting system based on clustering and decision trees, Decision Support Systems, 42, 408, 10.1016/j.dss.2005.01.008
Brown, 1962
Papalexopoulos, 1990, A regression-based approach to short-term system load forecasting, IEEE Transactions on Power Systems, 5, 1535, 10.1109/59.99410
Box, 1976
Winters, 1960, Forecasting sales by exponentially weighted moving averages, Management Science, 6, 324, 10.1287/mnsc.6.3.324
Abraham, 2009, vol. 234
Hui, 2016, Using artificial neural networks to improve decision making in apparel supply chain systems, 97
Zadeh, 1965, Fuzzy sets, Information and Control, 8, 338, 10.1016/S0019-9958(65)90241-X
Tan, 2006
Wong, 2010, A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm, International Journal of Production Economics, 128, 614, 10.1016/j.ijpe.2010.07.008
Au, 2008, Fashion retail forecasting by evolutionary neural networks, International Journal of Production Economics, 114, 615, 10.1016/j.ijpe.2007.06.013
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Larochelle, 2009, Exploring strategies for training deep neural networks, Journal of Machine Learning Research, 10, 1
Hu, 2018, Frankenstein: learning deep face representations using small data, IEEE Transactions on Image Processing, 27, 293, 10.1109/TIP.2017.2756450
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097
Dahl, 2012, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, IEEE Transactions on Audio, Speech and Language Processing, 20, 30, 10.1109/TASL.2011.2134090
Lusci, 2013, Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules, Journal of Chemical Information and Modeling, 53, 1563, 10.1021/ci400187y
Xu, 2015, Deep learning for drug-induced liver injury, Journal of Chemical Information and Modeling, 55, 2085, 10.1021/acs.jcim.5b00238
Ke, 2017, Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach, Transportation Research Part C, 85, 591, 10.1016/j.trc.2017.10.016
Huang, 2014, Deep architecture for traffic flow prediction: deep belief networks with multitask learning, IEEE Transactions on Intelligent Transportation Systems, 15, 2191, 10.1109/TITS.2014.2311123
Jiang, 2017, Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department, Expert Systems with Applications, 82, 216, 10.1016/j.eswa.2017.04.017
Qiu, 2017, Empirical mode decomposition based ensemble deep learning for load demand time series forecasting, Applied Soft Computing, 54, 246, 10.1016/j.asoc.2017.01.015
Kaneko, 2016, A deep learning approach for the prediction of retail store sales, 531
Lin, 2016
Koutsoukas, 2017, Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data, Journal of Cheminformatics, 9, 10.1186/s13321-017-0226-y
Chong, 2017, Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies, Expert Systems with Applications, 83, 187, 10.1016/j.eswa.2017.04.030
Fildes, 2006, The design features of forecasting support systems and their effectiveness, Decision Support Systems, 42, 351, 10.1016/j.dss.2005.01.003
Fildes, 2009, Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning, International Journal of Forecasting, 25, 3, 10.1016/j.ijforecast.2008.11.010
Coussement, 2015, A Bayesian approach for incorporating expert opinions into decision support systems: a case study of online consumer-satisfaction detection, Decision Support Systems, 79, 24, 10.1016/j.dss.2015.07.006
Franses, 2011, Combining SKU-level sales forecasts from models and experts, Expert Systems with Applications, 38, 2365, 10.1016/j.eswa.2010.08.024
Sinha, 2008, Incorporating domain knowledge into data mining classifiers: an application in indirect lending, Decision Support Systems, 46, 287, 10.1016/j.dss.2008.06.013
Ren, 2015, Fashion sales forecasting with a panel data-based particle-filter model, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 45, 411, 10.1109/TSMC.2014.2342194
Thomassey, 2007, A neural clustering and classification system for sales forecasting of new apparel items, Applied Soft Computing, 7, 1177, 10.1016/j.asoc.2006.01.005
Yu, 2011, An intelligent fast sales forecasting model for fashion products, Expert Systems with Applications, 38, 7373, 10.1016/j.eswa.2010.12.089
Tehrani, 2016, Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression, Journal of Retailing and Consumer Services, 32, 131, 10.1016/j.jretconser.2016.05.008
Efron, 1979, Bootstrap methods: another look at the jackknife, The Annals of Statistics, 7, 1, 10.1214/aos/1176344552
Smolensky, 1986
Quinlan, 1986, Induction of decision trees, Machine Learning, 1, 81, 10.1007/BF00116251
Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324
Hearst, 1998, Support vector machines, IEEE Intelligent Systems and Their Applications, 13, 18, 10.1109/5254.708428
McCulloch, 1943, A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, 5, 115, 10.1007/BF02478259
Witten, 2011, Data Mining: Practical Machine Learning Tools and Techniques
Han, 2011
Zeiler, 2012
Pal, 1992, Multilayer perceptron, fuzzy sets, and classification, IEEE Transactions on Neural Networks, 3, 683, 10.1109/72.159058
Bengio, 2009, Learning deep architectures for AI, Foundations and Trends® in Machine Learning, 2, 1, 10.1561/2200000006
Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, 15, 1929
Cortez, 2013, Using sensitivity analysis and visualization techniques to open black box data mining models, Information Sciences, 225, 1, 10.1016/j.ins.2012.10.039
Oztekin, 2013, A machine learning-based usability evaluation method for eLearning systems, Decision Support Systems, 56, 63, 10.1016/j.dss.2013.05.003
Delen, 2012, An analytic approach to better understanding and management of coronary surgeries, Decision Support Systems, 52, 698, 10.1016/j.dss.2011.11.004
Saltelli, 2004
Sevim, 2014, Developing an early warning system to predict currency crises, European Journal of Operational Research, 237, 1095, 10.1016/j.ejor.2014.02.047
Gardner, 1998, Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences, Atmospheric Environment, 32, 2627, 10.1016/S1352-2310(97)00447-0