A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales
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DOI: 10.1016/j.ijforecast.2021.11.006
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- Fokianos, Konstantinos & Rahbek, Anders & Tjøstheim, Dag, 2009.
"Poisson Autoregression,"
Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1430-1439.
- Konstantinos Fokianos & Anders Rahbek & Dag Tjøstheim, 2008. "Poisson Autoregression," Discussion Papers 08-35, University of Copenhagen. Department of Economics, revised Dec 2008.
- Konstantinos Fokianos & Anders Rahbek & Dag Tjøstheim, 2009. "Poisson Autoregression," CREATES Research Papers 2009-12, Department of Economics and Business Economics, Aarhus University.
- Cameron,A. Colin & Trivedi,Pravin K., 2013.
"Regression Analysis of Count Data,"
Cambridge Books,
Cambridge University Press, number 9781107667273, September.
- Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107014169, October.
- Jeffrey H Dorfman & Christian Gregory & Zhongyuan Liu & Ran Huo, 2019.
"Re‐Examining the SNAP Benefit Cycle Allowing for Heterogeneity,"
Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 41(3), pages 404-433, September.
- Jeffrey H Dorfman & Christian Gregory & Zhongyuan Liu & Ran Huo, 2019. "Re-Examining the SNAP Benefit Cycle Allowing for Heterogeneity," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 41(3), pages 404-433.
- Dorfman, Jeffrey H. & Gregory, Christian A. & Huo, Ran & Liu, Zhongyuan, 2017. "Re-Examining the SNAP Benefit Cycle Allowing for Heterogeneity," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258283, Agricultural and Applied Economics Association.
- Hilbe,Joseph M., 2014.
"Modeling Count Data,"
Cambridge Books,
Cambridge University Press, number 9781107028333, October.
- Hilbe,Joseph M., 2014. "Modeling Count Data," Cambridge Books, Cambridge University Press, number 9781107611252, October.
- Richard A. Davis & Rongning Wu, 2009. "A negative binomial model for time series of counts," Biometrika, Biometrika Trust, vol. 96(3), pages 735-749.
- Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
- Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
- Tammy Harris & Joseph M. Hilbe & James W. Hardin, 2014. "Modeling count data with generalized distributions," Stata Journal, StataCorp LP, vol. 14(3), pages 562-579, September.
- Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
- Narendra Agrawal & Stephen A. Smith, 1996. "Estimating negative binomial demand for retail inventory management with unobservable lost sales," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(6), pages 839-861, September.
- Paolo Gorgi, 2020. "Beta–negative binomial auto‐regressions for modelling integer‐valued time series with extreme observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1325-1347, December.
- Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
- Fukang Zhu, 2011. "A negative binomial integer‐valued GARCH model," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(1), pages 54-67, January.
- Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
- Christian Weiß, 2009. "Modelling time series of counts with overdispersion," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(4), pages 507-519, November.
- Syntetos, Aris A. & Boylan, John E., 2006. "On the stock control performance of intermittent demand estimators," International Journal of Production Economics, Elsevier, vol. 103(1), pages 36-47, September.
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Keywords
Sales forecasting; Probabilistic forecasting; Time series; Count data; M-competitions; State-space models; Exponential smoothing; Negative binomial;All these keywords.
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