A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach
Tóm tắt
Từ khóa
Tài liệu tham khảo
Kantasa-Ard A, Nouiri M, Bekrar A, Ait el Cadi A, Sallez Y (2021) Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand. Int J Prod Res 59(24):7491–7515
Haberleitner H, Meyr H, Taudes A (2010) Implementation of a demand planning system using advance order information. Int J Prod Econ 128(2):518–526
Tsoumakas G (2019) A survey of machine learning techniques for food sales prediction. Artif Intell Rev 52(1):441–447
Goecks J, Jalili V, Heiser LM, Gray JW (2020) How machine learning will transform biomedicine. Cell 181(1):92–101
Holzinger A (2016) Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inf 3(2):119–131
Bohanec M, Borštnar MK, Robnik-Šikonja M (2017) Explaining machine learning models in sales predictions. Expert Syst Appl 71:416–428
Chase CW Jr (2016) Machine learning is changing demand forecasting. J Bus Forecast 35(4):43
Ampazis N (2015) Forecasting demand in supply chain using machine learning algorithms. Int J Artif Life Res (IJALR) 5(1):56–73
Smolak K, Kasieczka B, Fialkiewicz W, Rohm W, Siła-Nowicka K, Kopańczyk K (2020) Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models. Urban Water J 17(1):32–42
Sillanpää V, Liesiö J (2018) Forecasting replenishment orders in retail: value of modelling low and intermittent consumer demand with distributions. Int J Prod Res 56(12):4168–4185
Mohammed A (2020) Towards ‘gresilient’ supply chain management: a quantitative study. Resour Conserv Recycl 155:104641
Oliva R, Watson N (2009) Managing functional biases in organizational forecasts: a case study of consensus forecasting in supply chain planning. Prod Oper Manag 18(2):138–151
Van der Laan E, van Dalen J, Rohrmoser M, Simpson R (2016) Demand forecasting and order planning for humanitarian logistics: an empirical assessment. J Oper Manag 45:114–122
Van Wassenhove LN, Pedraza Martinez AJ (2012) Using OR to adapt supply chain management best practices to humanitarian logistics. Int Trans Oper Res 19(1–2):307–322
Holt CC (2004) Forecasting seasonals and trends by exponentially weighted moving averages. Int J Forecast 20(1):5–10
Maia ALS, de Carvalho FDA (2011) Holt’s exponential smoothing and neural network models for forecasting interval-valued time series. Int J Forecast 27(3):740–759
Wang CH, Chen JY (2019) Demand forecasting and financial estimation considering the interactive dynamics of semiconductor supply-chain companies. Comput Ind Eng 138:106104
Jacobs FR, Chase RB, Lummus RR (2014) Operations and supply chain management (pp 533–535). New York, NY: McGraw-Hill/Irwin
Stevenson WJ, Hojati M, Cao J (2014) Operations management (p. 182). Chicago-USA: McGraw-Hill Education
Lu WM, Wang WK, Lee HL (2013) The relationship between corporate social responsibility and corporate performance: evidence from the US semiconductor industry. Int J Prod Res 51(19):5683–5695
Wang CH, Chen YW (2016) Combining balanced scorecard with data envelopment analysis to conduct performance diagnosis for Taiwanese LED manufacturers. Int J Prod Res 54(17):5169–5181
Addo-Tenkorang R, Helo PT (2016) Big data applications in operations/supply-chain management: a literature review. Comput Ind Eng 101:528–543
Hazen BT, Skipper JB, Ezell JD, Boone CA (2016) Big data and predictive analytics for supply chain sustainability: a theory-driven research agenda. Comput Ind Eng 101:592–598
Abolghasemi M, Hyndman RJ, Tarr G, Bergmeir C (2019) Machine learning applications in time series hierarchical forecasting. arXiv preprint arXiv:1912.00370
Abolghasemi M, Beh E, Tarr G, Gerlach R (2020) Demand forecasting in supply chain: the impact of demand volatility in the presence of promotion. Comput Ind Eng 142:106380
Aye GC, Balcilar M, Gupta R, Majumdar A (2015) Forecasting aggregate retail sales: the case of South Africa. Int J Prod Econ 160:66–79
Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H (2010) An empirical comparison of machine learning models for time series forecasting. Economet Rev 29(5–6):594–621
Punia S, Nikolopoulos K, Singh SP, Madaan JK, Litsiou K (2020) Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail. Int J Prod Res 58(16):4964–4979
Kang J, Guo X, Fang L, Wang X, Fan Z (2021) Integration of Internet search data to predict tourism trends using spatial-temporal XGBoost composite model. Int J Geogr Inf Sci 36(2):236–252
Xenochristou M, Hutton C, Hofman J, Kapelan Z (2020) Water demand forecasting accuracy and influencing factors at different spatial scales using a gradient boosting machine. Water Resources Res 56(8):e2019WR026304
Walker KW, Jiang Z (2019) Application of adaptive boosting (AdaBoost) in demand-driven acquisition (DDA) prediction: a machine-learning approach. J Acad Librariansh 45(3):203–212
Jahangir H, Tayarani H, Ahmadian A, Golkar MA, Miret J, Tayarani M, Gao HO (2019) Charging demand of plug-in electric vehicles: forecasting travel behaviour based on a novel rough artificial neural network approach. J Clean Prod 229:1029–1044
Islam S, Amin SH (2020) Prediction of probable backorder scenarios in the supply chain using distributed random forest and gradient boosting machine learning techniques. J Big Data 7(1):1–22
Mueller SQ (2020) Pre-and within-season attendance forecasting in Major League Baseball: a random forest approach. Appl Econ 52(41):4512–4528
Li C, Tao Y, Ao W, Yang S, Bai Y (2018) Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition. Energy 165:1220–1227
Rao C, Liu M, Goh M, Wen J (2020) 2-stage modified random forest model for credit risk assessment of P2P network lending to “Three Rurals” borrowers. Appl Soft Comput 95:106570
Ni L, Wang D, Wu J, Wang Y, Tao Y, Zhang J, Liu J (2020) Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model. J Hydrol 586:124901
Wang Y, Sun S, Chen X, Zeng X, Kong Y, Chen J, Wang T (2021) Short-term load forecasting of industrial customers based on SVMD and XGBoost. Int J Electr Power Energy Syst 129:106830
Yun KK, Yoon SW, Won D (2021) Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Syst Appl 186:115716
Wang Z, Hong T, Piette MA (2020) Building thermal load prediction through shallow machine learning and deep learning. Appl Energy 263:114683
Jabeur SB, Mefteh-Wali S, Viviani JL (2021) Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Ann Operations Res 1–21
Osman AIA, Ahmed AN, Chow MF, Huang YF, El-Shafie A (2021) Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Eng J 12(2):1545–1556
Shi R, Xu X, Li J, Li Y (2021) Prediction and analysis of train arrival delay based on XGBoost and Bayesian optimization. Appl Soft Comput 109:107538
Zhou L, Lai KK (2017) AdaBoost models for corporate bankruptcy prediction with missing data. Comput Econ 50(1):69–94
Barrow DK, Crone SF (2016) A comparison of AdaBoost algorithms for time series forecast combination. Int J Forecast 32(4):1103–1119
Wang L, Lv SX, Zeng YR (2018) Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China. Energy 155:1013–1031
Sidhu RK, Kumar R, Rana PS (2020) Machine learning based crop water demand forecasting using minimum climatological data. Multimed Tools Appl 79(19):13109–13124
Busari GA, Lim DH (2021) Crude oil price prediction: a comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance. Comput Chem Eng 155:107513
Huang H, Zhang Z, Song F (2021) An ensemble-learning-based method for short-term water demand forecasting. Water Resour Manage 35(6):1757–1773
Sun S, Wei Y, Wang S (2018) AdaBoost-LSTM ensemble learning for financial time series forecasting. Int Conf Comput Sci (pp 590–597). Springer, Cham
Heo J, Yang JY (2014) AdaBoost based bankruptcy forecasting of Korean construction companies. Appl Soft Comput 24:494–499
Sharma V, Cali Ü, Sardana B, Kuzlu M, Banga D, Pipattanasomporn M (2021) Data-driven short-term natural gas demand forecasting with machine learning techniques. J Petrol Sci Eng 206:108979
Deng S, Wang C, Wang M, Sun Z (2019) A gradient boosting decision tree approach for insider trading identification: an empirical model evaluation of China stock market. Appl Soft Comput 83:105652
Yoon J (2021) Forecasting of real GDP growth using machine learning models: gradient boosting and random forest approach. Comput Econ 57(1):247–265
Gu Q, Chang Y, Xiong N, Chen L (2021) Forecasting nickel futures price based on the empirical wavelet transform and gradient boosting decision trees. Appl Soft Comput 109:107472
Nie P, Roccotelli M, Fanti MP, Ming Z, Li Z (2021) Prediction of home energy consumption based on gradient boosting regression tree. Energy Rep 7:1246–1255
Güven İ, Şimşir F (2020) Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods. Comput Ind Eng 147:106678
Yucesan M, Gul M, Celik E (2018) A multi-method patient arrival forecasting outline for hospital emergency departments. Int J Healthcare Manage 13(Sup1):283–295
Fanoodi B, Malmir B, Jahantigh FF (2019) Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Comput Biol Med 113:103415
Jebaraj S, Iniyan S, Goic R (2011) Forecasting of coal consumption using an artificial neural network and comparison with various forecasting techniques. Energy Sources Part A Recov Util Environ Effects 33(14):1305–1316
Zhao X, Yue S (2021) Analysing and forecasting the security in supply-demand management of Chinese forestry enterprises by linear weighted method and artificial neural network. Enterprise Inf Syst 15(9):1280–1297
Loureiro AL, Miguéis VL, da Silva LF (2018) Exploring the use of deep neural networks for sales forecasting in fashion retail. Decis Support Syst 114:81–93
Chicco D, Warrens MJ, Jurman G (2021) The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci 7:e623
Ala’raj M, Majdalawieh M, Nizamuddin N (2021) Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections. Infect Dis Model 6:98–111
Ramos P, Santos N, Rebelo R (2015) Performance of state space and ARIMA models for consumer retail sales forecasting. Robot Comput Integr Manuf 34:151–163
Parsa AB, Movahedi A, Taghipour H, Derrible S, Mohammadian AK (2020) Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accid Anal Prev 136:105405
Zhang Y, Haghani A (2015) A gradient boosting method to improve travel time prediction. Transport Res Part C Emerg Technol 58:308–324
Lahouar A, Slama JBH (2015) Day-ahead load forecast using random forest and expert input selection. Energy Convers Manage 103:1040–1051
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system, in proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). San Francisco, CA, 785–794
Kaplan UE, Dagasan Y, Topal E (2021) Mineral grade estimation using gradient boosting regression trees. Int J Min Reclam Environ 35(10):728–742
Ren S, Cao X, Wei Y, Sun J (2015) Global refinement of random forest. Proc IEEE Conf Comput Vision Pattern Recogn 723–730
Samat A, Li E, Wang W, Liu S, Lin C, Abuduwaili J (2020) Meta-XGBoost for hyperspectral image classification using extended MSER-guided morphological profiles. Remote Sens 12(12):1973
Jabbar H, Khan RZ (2015) Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study). Computer Sci Commun Instrument Devices 70
Steyerberg EW (2019) Overfitting and optimism in prediction models. Clin Predict Models (pp 95–112). Springer, Cham.)
Ardabili S, Mosavi A, Várkonyi-Kóczy AR (2019) Advances in machine learning modeling reviewing hybrid and ensemble methods. In International Conference on Global Research and Education (pp 215–227). Springer, Cham
Wang Z, Bovik AC (2009) Mean squared error: love it or leave it? A new look at signal fidelity measures. IEEE Signal Process Mag 26(1):98–117
Valbuena R, Hernando A, Manzanera JA, Görgens EB, Almeida DR, Silva CA, García-Abril A (2019) Evaluating observed versus predicted forest biomass: R-squared, index of agreement or maximal information coefficient? Eur J Remote Sens 52(1):345–358