{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:55:56Z","timestamp":1774630556019,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>In this paper, we study the usage of machine-learning models for sales predictive analytics. The main goal of this paper is to consider main approaches and case studies of using machine learning for sales forecasting. The effect of machine-learning generalization has been considered. This effect can be used to make sales predictions when there is a small amount of historical data for specific sales time series in the case when a new product or store is launched. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking techniques, we can improve the performance of predictive models for sales time series forecasting.<\/jats:p>","DOI":"10.3390\/data4010015","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T05:41:08Z","timestamp":1547790068000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":169,"title":["Machine-Learning Models for Sales Time Series Forecasting"],"prefix":"10.3390","volume":"4","author":[{"given":"Bohdan","family":"Pavlyshenko","sequence":"first","affiliation":[{"name":"SoftServe, Inc., 2D Sadova St., 79021 Lviv, Ukraine"},{"name":"Ivan Franko National University of Lviv, 1, Universytetska St., 79000 Lviv, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mentzer, J.T., and Moon, M.A. (2004). Sales Forecasting Management: A Demand Management Approach, Sage.","DOI":"10.4135\/9781452204444"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6697","DOI":"10.1016\/j.eswa.2008.08.058","article-title":"A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis","volume":"36","author":"Efendigil","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, G.P. (2004). Neural Networks in Business Forecasting, IGI Global.","DOI":"10.4018\/978-1-59140-176-6"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chatfield, C. (2000). Time-Series Forecasting, Chapman and Hall\/CRC.","DOI":"10.1201\/9781420036206"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Brockwell, P.J., Davis, R.A., and Calder, M.V. (2002). Introduction to Time Series and Forecasting, Springer.","DOI":"10.1007\/b97391"},{"key":"ref_6","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, John Wiley & Sons."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.jfoodeng.2005.03.056","article-title":"Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing","volume":"75","author":"Doganis","year":"2006","journal-title":"J. Food Eng."},{"key":"ref_8","unstructured":"Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice, OTexts."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tsay, R.S. (2005). Analysis of Financial Time Series, John Wiley & Sons.","DOI":"10.1002\/0471746193"},{"key":"ref_10","unstructured":"Wei, W.W. (2006). Time series analysis. The Oxford Handbook of Quantitative Methods in Psychology: Volume 2, Oxford University Press."},{"key":"ref_11","first-page":"1","article-title":"Arbitrage of forecasting experts","volume":"1","author":"Cerqueira","year":"2018","journal-title":"Mach. Learn."},{"key":"ref_12","unstructured":"Hyndman, R.J., and Khandakar, Y. (2007). Automatic Time Series for Forecasting: The Forecast Package for R, Monash University, Department of Econometrics and Business Statistics. Number 6\/07."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Papacharalampous, G.A., Tyralis, H., and Koutsoyiannis, D. (2017). Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes. J. Hydrol., 10.","DOI":"10.20944\/preprints201710.0133.v1"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tyralis, H., and Papacharalampous, G. (2017). Variable selection in time series forecasting using random forests. Algorithms, 10.","DOI":"10.3390\/a10040114"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"147","DOI":"10.5194\/adgeo-45-147-2018","article-title":"Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow","volume":"45","author":"Tyralis","year":"2018","journal-title":"Adv. Geosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1007\/s11600-018-0120-7","article-title":"Predictability of monthly temperature and precipitation using automatic time series forecasting methods","volume":"66","author":"Papacharalampous","year":"2018","journal-title":"Acta Geophys."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1016\/j.eswa.2012.01.039","article-title":"A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition","volume":"39","author":"Taieb","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.ijforecast.2013.02.005","article-title":"Combining forecasts: An application to elections","volume":"30","author":"Graefe","year":"2014","journal-title":"Int. J. Forecast."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","article-title":"Ensemble-based classifiers","volume":"33","author":"Rokach","year":"2010","journal-title":"Artif. Intell. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_22","first-page":"23","article-title":"A survey on ensemble learning for data stream classification","volume":"50","author":"Gomes","year":"2017","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000, January 21\u201323). Ensemble methods in machine learning. Proceedings of the International Workshop on Multiple Classifier Systems, Cagliari, Italy.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rokach, L. (2005). Ensemble methods for classifiers. Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/0-387-25465-X_45"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/0169-2070(89)90013-7","article-title":"Combining forecasts: The end of the beginning or the beginning of the end?","volume":"5","author":"Armstrong","year":"1989","journal-title":"Int. J. Forecast."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5207","DOI":"10.1007\/s11269-018-2155-6","article-title":"Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: A multiple-case study from Greece","volume":"32","author":"Papacharalampous","year":"2018","journal-title":"Water Resour. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pavlyshenko, B.M. (2016, January 23\u201327). Linear, machine learning and probabilistic approaches for time series analysis. Proceedings of the IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP.2016.7583582"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pavlyshenko, B. (2016, January 5\u20138). Machine learning, linear and Bayesian models for logistic regression in failure detection problems. Proceedings of the 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA.","DOI":"10.1109\/BigData.2016.7840828"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pavlyshenko, B. (2018, January 21\u201325). Using Stacking Approaches for Machine Learning Models. Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP.2018.8478522"},{"key":"ref_34","unstructured":"(2018, November 03). \u2019Rossmann Store Sales\u2019, Kaggle.Com. Available online: http:\/\/www.kaggle.com\/c\/rossmann-store-sales."},{"key":"ref_35","unstructured":"(2018, November 03). Kaggle: Your Home for Data Science. Available online: http:\/\/kaggle.com."},{"key":"ref_36","unstructured":"(2018, November 03). Kaggle Competition \u2019Grupo Bimbo Inventory Demand\u2019. Available online: https:\/\/www.kaggle.com\/c\/grupo-bimbo-inventory-demand."},{"key":"ref_37","unstructured":"(2018, November 03). Kaggle Competition \u2019Grupo Bimbo Inventory Demand\u2019 #1 Place Solution of The Slippery Appraisals Team. Available online: https:\/\/www.kaggle.com\/c\/grupo-bimbo-inventory-demand\/discussion\/23863."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_39","unstructured":"(2018, November 03). Kaggle Competition \u2019Grupo Bimbo Inventory Demand\u2019 Bimbo XGBoost R Script LB:0.457. Available online: https:\/\/www.kaggle.com\/bpavlyshenko\/bimbo-xgboost-r-script-lb-0-457."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/1\/15\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:27:06Z","timestamp":1760185626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/1\/15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,18]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["data4010015"],"URL":"https:\/\/doi.org\/10.3390\/data4010015","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints201811.0096.v1","asserted-by":"object"}]},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,18]]}}}