Deep Learning Applications in Sales Forecasting
Referanslar
Ansuj, A. P., Camargo, M. E., Radharamanan, R., et al. (1996). Sales forecasting using time series and neural networks. Computers & Industrial Engineering, 31(1-2), 421-424. Doi: 10.1016/0360-8352(96)00166-0.
Antipov, E.A., Pokryshevskaya, E.B. (2020). Interpretable machine learning for demand modeling with high-dimensional data using Gradient Boosting Machines and Shapley values. Journal of Revenue and Pricing Management, 19, 355–364. Doi: 10.1057/s41272-020-00236-4.
Behera, G., Nain, N. (2019). A comparative study of big mart sales prediction. In International Conference on Computer Vision and Image Processing (pp. 421-432). Springer, Singapore. DOI: 10.1007/978-981-15-4015-8_37
Brühl, B., Hülsmann, M., Borscheid, D., et al. (2009). A sales forecast model for the German automobile market based on time series analysis and data mining methods. In Industrial Conference on Data Mining (pp. 146-160). Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-03067-3_13.
Chakraborty, K., Mehrotra, K., Mohan, C. K., et al. (1992). Forecasting the behavior of multivariate time series using neural networks. Neural networks, 5(6), 961-970. Doi: 10.1016/S0893-6080(05)80092-9.
Chang, P. C., Wang, Y. W., Tsai, C. Y. (2005). Evolving neural network for printed circuit board sales forecasting. Expert Systems with Applications, 29(1), 83-92. Doi: 10.1016/j.eswa.2005.01.012.
Cheng, J., Hong, T., Li, X., et al. (2018). Research on Sales Forecasting Method for Transmission Parts of Customized Production. 3rd International Conference on Communications, Information Management and Network Security (CIMNS 2018) (pp. 220-224). Atlantis Press. Doi: 10.2991/cimns-18.2018.50
Cho, K., Van Merriënboer, B., Gulcehre, et al (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Chu, C. W., Zhang, G. P. (2003). A comparative study of linear and nonlinear models for aggregate retail sales forecasting. International Journal of production economics, 86(3), 217-231. Doi:10.1016/S0925-5273(03)00068-9
Crone, S. F., Lessmann, S., Pietsch, S. (2006). Forecasting with computational intelligence-an evaluation of support vector regression and artificial neural networks for time series prediction. In The 2006 IEEE International Joint Conference on Neural Network Proceedings (pp. 3159-3166). DOI: 10.1109/IJCNN.2006.247299
Dai, Y., & Huang, J. (2021). A Sales Prediction Method Based on LSTM with Hyper-Parameter Search. In Journal of Physics: Conference Series (Vol. 1756, No. 1, p. 012015). IOP Publishing. DOI:10.1088/1742-6596/1756/1/012015
Doganis, P., Alexandridis, A., Patrinos, P., et al. (2006). Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. Journal of Food Engineering, 75(2), 196-204. Doi: 10.1016/j.jfoodeng. 2005.03.056
Ensafi, Y., Amin, S. H., Zhang, G., et al. (2022). Time-series forecasting of seasonal items sales using machine learning–A comparative analysis. International Journal of Information Management Data Insights, 2(1). Doi: 10.1016/j.jjimei.2022.100058
Frees, E. W., Miller, T. W. (2004). Sales forecasting using longitudinal data models. International Journal of Forecasting, 20(1), 99-114. Doi: 10.1016/S0169-2070(03)00005-0.
Guajardo, J. A., Weber, R., Miranda, J. (2010). A model updating strategy for predicting time series with seasonal patterns. Applied Soft Computing, 10(1), 276-283. Doi: 10.1016/j.asoc.2009.07.005
Han, Y. (2020). A forecasting method of pharmaceutical sales based on ARIMA-LSTM model. In 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT) (pp. 336-339). Doi: 10.1109/ISCTT51595.2020.00064
He, Q. Q., Wu, C., Si, Y. W. (2022). LSTM with Particle Swam Optimization for Sales Forecasting. Electronic Commerce Research and Applications, 101118. Doi: 10.1016/j.elerap.2022.101118
He, Z., Yu, S. (2020). Application of LightGBM and LSTM combined model in vegetable sales forecast. In Journal of Physics: Conference Series (Vol. 1693, No. 1, p. 012110). IOP Publishing. Doi: 10.1088/1742-6596/1693/1/012110
Helmini, S., Jihan, N., Jayasinghe, et al. (2019). Sales forecasting using multivariate long short term memory network models. PeerJ, PrePrints, 7, e27712v1. Doi: 10.7287/ peerj.preprints.27712v1
Hong, J. K. (2021). LSTM-based Sales Forecasting Model. KSII Transactions on Internet and Information Systems (TIIS), 15(4), 1232-1245. Doi: 10.3837/tiis.2021.04.003
Karmy, J. P., Maldonado, S. (2019). Hierarchical time series forecasting via support vector regression in the European travel retail industry. Expert Systems with Applications, 137, 59-73. Doi: 10.1016/j.eswa.2019.06.060
Karpathy, A., Johnson, J., Fei-Fei, L. (2015). Visualizing and understanding recurrent networks. arXiv preprint arXiv:1506.02078. Doi: 10.48550/arXiv.1506.02078
Kohli, S., Godwin, G. T., Urolagin, S. (2021). Sales Prediction Using Linear and KNN Regression. Advances in Machine Learning and Computational Intelligence, 321. Doi: 10.1007/978-981-15-5243-4_29
Kuo, R. J. (2001). A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm. European Journal of Operational Research, 129(3), 496-517. Doi: 10.1016/S0377-2217(99)00463-4
Lu, C. J., Lee, T. S., Lian, C. M. (2010). Sales forecasting of IT products using a hybrid MARS and SVR model. In 2010 IEEE International Conference on Data Mining Workshops (pp. 593-599). Doi: 10.1109/ICDMW.2010.11
Ma, S., Fildes, R. (2021). Retail sales forecasting with meta-learning. European Journal of Operational Research, 288(1), 111-128. Doi: 10.1016/j.ejor.2020.05.038
Ma, Z., Wang, C., Zhang, Z. (2021). Deep Learning Algorithms for Automotive Spare Parts Demand Forecasting. In 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI) (pp. 358-361). IEEE. Doi: 10.1109/CISAI54367.2021.00075
Massaro, A., Panarese, A., Giannone, D., et al. (2021). Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector. Applied Sciences, 11(17), 7793. Doi: 10.3390/app11177793
Mentzer, J. T., Cox Jr, J. E. (1984). Familiarity, application, and performance of sales forecasting techniques. Journal of forecasting, 3(1), 27-36.
Mentzer, J. T., Moon, M. A., Kent, J. L., et al. (1997). The need for a forecasting champion. The Journal of Business Forecasting, 16(3), 3.
Natekin, A., Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21. Doi: 10.3389/fnbot.2013.00021.
O'Shea, K., Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458. Doi: 10.48550/arXiv.1511.08458.
Pemathilake, R. G. H., Karunathilake, S. P., Shamal, et al. (2018). Sales forecasting based on autoregressive integrated moving average and recurrent neural network hybrid model. In 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (pp. 27-33). IEEE. Doi: 10.1109/FSKD.2018.8686936
Qi, Y., Li, C., Deng, H., et al. (2019). A deep neural framework for sales forecasting in e-commerce. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp.299-308). Doi: 10.1145/3357384.3357883
Sanders, N. R., Ritzman, L. P. (2004). Integrating judgmental and quantitative forecasts: methodologies for pooling marketing and operations information. International Journal of Operations & Production Management, 24(5), 514-529. Doi: 10.1108/01443570410532560.
Schmidt, A., Kabir, M. W. U., Hoque, M. T. (2022). Machine Learning Based Restaurant Sales Forecasting. Machine Learning and Knowledge Extraction, 4(1), 105-130. Doi: 10.3390/make4010006
Shilong, Z. (2021). Machine learning model for sales forecasting by using XGBoost. In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 480-483). IEEE. Doi: 10.1109/ICCECE51280.2021.9342304
Shih, Y. S., Lin, M. H. (2019). A LSTM approach for sales forecasting of goods with short-term demands in E-commerce. In Asian Conference on Intelligent Information and Database Systems (pp. 244-256). Springer, Cham. Doi: 10.1007/978-3-030-14799-0_21
Sun, Z. L., Choi, T. M., Au, K. F., et al. (2008). Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems, 46(1), 411-419. Doi: 10.1016/j.dss.2008.07.009
Tang, X., Gao, S., & Jiang, Z. (2019). A Blending Model Combined DNN and LightGBM for Forecasting the Sales of Airline Tickets. In Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence (pp. 150-154). Doi: 10.1145/3374587.3374652
Tang, Z., De Almeida, C., Fishwick, P. A. (1991). Time series forecasting using neural networks vs. Box-Jenkins methodology. Simulation, 57(5), 303-310. Doi: 10.1177/003754979105700508
Temur, A. S., Akgün, M., & Temur, G. (2019). Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models. Doi: 10.3846/jbem.2019.10190
Thiesing, F. M., Vornberger, O. (1997). Sales forecasting using neural networks. In Proceedings of International Conference on Neural Networks (ICNN'97) (Vol. 4, pp. 2125-2128). IEEE. Doi: 10.1109/ICNN.1997.614234
Torres, J. F., Hadjout, D., Sebaa, A., et al. (2021). Deep learning for time series forecasting: a survey. Big Data, 9(1), 3-21. Doi: 10.1089/big.2020.0159
Vavliakis, K. N., Siailis, A., Symeonidis, A. L. (2021). Optimizing Sales Forecasting in e-Commerce with ARIMA and LSTM Models. In Proceedings of the 17th International Conference on Web Information Systems and Technologies (WEBIST 2021), pages 299-306. Doi: 10.5220/0010659500003058
Wang, C. H. (2022). Considering economic indicators and dynamic channel interactions to conduct sales forecasting and for retail sectors. Computers & Industrial Engineering, 107965. Doi: 10.1016/j.cie.2022.107965
Wang, S. and Yang, Y. (2021), M-GAN-XGBOOST model for sales prediction and precision marketing strategy making of each product in online stores. Data Technologies and Applications, Vol. 55 No. 5, pp. 749-770. Doi: 10.1108/DTA-11-2020-0286
Wang, Y., Chang, D., Zhou, C. (2019). The study of a sales forecast model based on SA-LSTM. In Journal of Physics: Conference Series (Vol. 1314, No. 1, p. 012215). IOP Publishing. Doi:10.1088/1742-6596/1314/1/012215
Weng, T., Liu, W., Xiao, J. (2020), Supply chain sales forecasting based on lightGBM and LSTM combination model. Industrial Management & Data Systems, Vol. 120 No. 2, pp. 265-279. Doi: 10.1108/IMDS-03-2019-0170
West, D. C. (1994). Number of sales forecast methods and marketing management. Journal of Forecasting, 13(4), 395. Doi: 10.1002/for.3980130405
Xu, J., Zhou, Y., Zhang, L., et al. (2021). Sportswear retailing forecast model based on the combination of multi-layer perceptron and convolutional neural network. Textile Research Journal, 91(23-24):2980-2994. Doi: 10.1177/00405175211020518
Yu, Q., Wang, K., Strandhagen, J. O., et al. (2017). Application of long short-term memory neural network to sales forecasting in retail—a case study. In International Workshop of Advanced Manufacturing and Automation (pp. 11-17). Springer, Singapore. Doi: 10.1007/978-981-10-5768-7_2
Yuan, F. C. (2012). Parameters Optimization Using Genetic Algorithms in Support Vector Regression for Sales Volume Forecasting. Applied Mathematics, 3, 1480-1486. Doi: 10.4236/am.2012.330207
Zhao, B., Lu, H., Chen, S., et al. (2017). Convolutional neural networks for time series classification. Journal of Systems Engineering and Electronics, 28(1), 162-169. Doi: 10.21629/JSEE.2017.01.18
Zhao, Y., Ren, X., Zhang, X. (2021). Optimization of a Comprehensive Sequence Forecasting Framework Based on DAE-LSTM Algorithm. In Journal of Physics: Conference Series (Vol. 1746, No. 1, p. 012087). IOP Publishing. Doi:10.1088/1742-6596/1746/1/012087
Zheng, D., Zhang, W., Netsanet, S., et al. (2021). Short-term renewable generation and load forecasting in microgrids. Dehua Zheng (Ed)., Microgrid Protection and Control, (pp. 57–96). Academic Press. Doi: 10.1016/B978-0-12-821189-2.00005-X