Interpreting direct sales’ demand forecasts using SHAP values
Mariana Arboleda-Florez; Carlos Castro-Zuluaga
Abstract
Keywords
References
Abdulhai, B., & Kattan, L. (2003). Reinforcement learning: introduction to theory and potential for transport applications.
Adadi, A., & Berrada, M. (2018). Peeking inside the black box: a survey on explainable artificial intelligence (XAI). In
Bandeira, S., Alcalá, S., Vita, R., & Barbosa, T. (2020). Comparison of selection and combination strategies for demand forecasting methods.
Barredo Arrieta, A., Díaz-Rodríguez, N., del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities, and challenges toward responsible AI.
Bertrand, J. W. M., & Fransoo, J. C. (2002). Operations management research methodologies using quantitative modeling.
Bisong, E. (2019). What is machine learning? In E. Bisong.
Breiman, L. (2001). Random forests.
Brockwell, P. J., & Davis, R. A. (1987).
Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications.
Bugaj, M., Wrobel, K., & Iwaniec, J. (2021, May 12-16). Model explainability using SHAP values for LightGBM predictions. In
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting.
Castro-Zuluaga, C. A., & Arboleda, M. (2019). Sales forecasting difficulties' analysis on colombian direct sales companies. In M. T. Castañeda Galvis, J. Nuñez Rodriguez, M. C. Pérez Ordoñez & M. Villa Marulanda (Eds.),
Castro-Zuluaga, C., & Arboleda-Florez, M. (2021). Introduction. In M. Hemmati & M. S. Sajadieh (Eds.),
Chatfield, C. (2000).
Chen, I. F., & Lu, C. J. (2021). Demand forecasting for multichannel fashion retailers by integrating clustering and machine learning algorithms.
Clinciu, M. A., & Hastie, H. (2019). A survey of explainable AI terminology. In
Crum, C., & Palmatier, G. E. (2003).
Dairu, X., & Shilong, Z. (2021). Machine learning model for sales forecasting by using XGBoost. In
Deloitte. (2017).
Dong, G., & Liu, H. (Eds.). (2018).
Friedman, J., & Popescu, B. E. (2003). Gradient directed regularization for linear regression and classification. Technical Report, Statistics Department, Stanford University.
Gartner Inc. (2017).
Gilbert, F. (2019).
Gramegna, A., & Giudici, P. (2021). SHAP and LIME: an evaluation of discriminative power in credit risk.
Gumani, M., Korke, Y., Shah, P., Udmale, S., Sambhe, V., & Bhirud, S. (2017). Forecasting of sales by using fusion of machine learning techniques. In
Hiziroglu, A. (2013). Soft computing applications in customer segmentation: state-of-art review and critique.
Ishikawa, F., & Yoshioka, N. (2019). How Do Engineers Perceive Difficulties in Engineering of Machine-Learning Systems? - Questionnaire Survey. In
Jeon, Y., & Seong, S. (2021). Robust recurrent network model for intermittent time-series forecasting.
Kormushev, P., Calinon, S., & Caldwell, D. G. (2013). Reinforcement learning in robotics: applications and real-world challenges.
Krishna, A., Akhilesh, V., Aich, A., & Hegde, C. (2018). Sales-forecasting of Retail Stores using Machine Learning Techniques. In
Ktenioudaki, A., O’Donnell, C. P., Emond, J. P., & do Nascimento Nunes, M. C. (2021). Blueberry supply chain: critical steps impacting fruit quality and application of a boosted regression tree model to predict weight loss.
Kumar, V., & Boulanger, D. (2020). Explainable automated essay scoring: deep learning really has pedagogical value.
Lorente-Leyva, L. L., Alemany, M. M. E., Peluffo-Ordóñez, D. H., & Araujo, R. A. (2021). Demand forecasting for textile products using statistical analysis and machine learning algorithms. In N. T. Nguyen, S. Chittayasothorn, D. Niyato & B. Trawiński (Eds.),
Lundberg, S. (2019).
Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2019). Consistent individualized feature attribution for tree ensembles.
Lundberg, S., & Lee, S.-I. (2017). A unified approach to interpreting model predictions.
Makridakis, S. (1988). Metaforecasting.
Marcilio, W. E., & Eler, D. M. (2020). From explanations to feature selection: assessing SHAP values as feature selection mechanism. In
Meng, Y., Yang, N., Qian, Z., & Zhang, G. (2020). What makes an online review more helpful: an interpretation framework using XGBoost and shap values.
Mitchell, T. M. (1997).
Mitchell, T. M. (2006).
Moore, J. D., & Swartout, W. R. (1988).
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: machine Learning in Python.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2019).
Raschka, S., & Mirjalili, V. (2015).
Ren, S., Chan, H. L., & Siqin, T. (2020). Demand forecasting in retail operations for fashionable products: methods, practices, and real case study.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Model-agnostic interpretability of machine learning.
Samek, W., & Müller, K.-R. (2019). Towards explainable artificial intelligence. In W. Samek, G. Montavon, A. Vedaldi, L. Hansen & K. R. Müller (Eds.),
Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., & Young, M. (2014). Machine learning: the high-interest credit card of technical Debt. In
Seaman, B., & Bowman, J. (2021). Applicability of the M5 to Forecasting at Walmart.
Seeling, M. X., Scavarda, L. F., & Thomé, A. M. T. (2019). A sales and operations planning application in the Brazilian subsidiary of a multinational chemical company.
Shams Amiri, S., Mottahedi, S., Lee, E. R., & Hoque, S. (2021). Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption.
Sheshasaayee, A., & Logeshwari, L. (2018). Implementation of clustering technique based RFM analysis for customer behaviour in online transactions. In
Silver, E. A., Pyke, D. F., & Thomas, D. J. (2016).
Strumbelj, E., & Kononenko, I. (2010). An efficient explanation of individual classifications using game theory.
Su, X., Yan, X., & Tsai, C. L. (2012). Linear regression.
Sun, Z. L., Choi, T. M., Au, K. F., & Yu, Y. (2008). Sales forecasting using extreme learning machine with applications in fashion retailing.
Sutton, R., & Barto, A. G. (2018).
Tarallo, E., Akabane, G. K., Shimabukuro, C. I., Mello, J., & Amancio, D. (2019). Machine learning in predicting demand for fast-moving consumer goods: an exploratory research.
Tirkolaee, E. B., Sadeghi, S., Mooseloo, F. M., Vandchali, H. R., & Aeini, S. (2021). Application of machine learning in supply chain management: a comprehensive overview of the main areas.
Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. Ch. (2015). A comparison of machine learning techniques for customer churn prediction.
Vogel, W., & Lasch, R. (2016). Complexity drivers in manufacturing companies: a literature review.
Wenzel, H., Smit, D., & Sardesai, S. (2019). A literature review on machine learning in supply chain management. In W. Kersten, T. Blecker & C. M. Ringle (Eds.),
Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y.-F., Tu, W.-W., Yang, Q., & Yu, Y. (2018). Taking human out of learning applications: a survey on automated machine learning.
Zhang, B., & Ma, D. (2020). Flight delay prediciton at an airport using maching learning. In
Submitted date:
03/04/2022
Accepted date:
11/29/2022