References
- Bauer, E. and R. Kohavi, "An empirical comparison of voting classification algorithms: bagging, boosting, and variants," Machine Learning 36, 1/2 (1999), 105-139. https://doi.org/10.1023/A:1007515423169
- Boylan, J. E., A. A. Syntetos, and G. C. Karakostas, "Classification for forecasting and stock control: a case study," Journal of the Operational Research Society 59 (2008), 473- 481. https://doi.org/10.1057/palgrave.jors.2602312
- Breiman, L., "Random forests," Machine Learning 45, 1(2001), 5-32. https://doi.org/10.1023/A:1010933404324
- Breiman, L., J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees, Chapman and Hall, NY, USA, 1984.
- Caruana, R. and A. Niculescu-Mizil, An empirical comparison of supervised learning algorithms using different performance metrics, Pittsburgh, USA: 2006.
- Chatfield, C., The Analysis of Time Series: an Introduction, 6th edn., Chapman and Hall/CRC, London, UK, 2004.
- Chen, H. and J. E. Boylan, The effect of correlation between demands on hierarchical forecasting, In: Lawrence, K. D. Klimberg, R. K. (Eds.), Advances in business and management forecasting, Bingley, UK: Emerald Group Publishing Limited, (2009), 173-188.
- Culp, M., K. Johnson, G. Michailidis, ada: an R package for stochastic boosting, Available at: http://cran.ma.imperial.ac.uk/, Accessed on 4 June 2012.
- Dekker, M., K. V. Donselaar, and A. P. Ouwehand, "How to use aggregation and combined forecasting to improve seasonal demand forecasts," International Journal of Production Economic 90, 2 (2004), 151-167. https://doi.org/10.1016/j.ijpe.2004.02.004
- Dreiseitl, S. and L. Ohno-Machado, "Logistic regression and artificial neural network classification models: a methodology review," Journal of Biomedical Informatics 35, 5/6 (2002), 352-359. https://doi.org/10.1016/S1532-0464(03)00034-0
- Fildes, R., K. Nikolopoulos, S. F. Crone, and A. A. Syntetos, "Forecasting and operational research: a review," Journal of the Operational Research Society 59 (2008), 1150-1172. https://doi.org/10.1057/palgrave.jors.2602597
- Fliedner, E. B. and B. Lawrence, "Forecasting system parent group formation: An empirical application of cluster analysis," Journal of Operations Management 12, 2 (1995), 119-130. https://doi.org/10.1016/0272-6963(94)00009-4
- Fliedner, E. B. and Mabert, "Constrained forecasting: some implementation guidelines," Decision Sciences 23, 5 (1992), 1143-1161. https://doi.org/10.1111/j.1540-5915.1992.tb00440.x
- Fox, J., R Commander 1.5-5, Available at: http://cran.ma.imperial.ac.uk/, Accessed on 4 June 2012.
- Freund, Y. and R. E. Schapire, "A short introduction to boosting," Journal of Japanese Society for Artificial Intelligence 14, 5 (1999), 771-780.
- Friedman, J., T. Hastie, and R. Tibshirani, "Additive logistic regression: a statistical view of boosting," Annals of Statistics 28, 2 (2000), 337-407.
- Hyndman, R. J., R. A. Ahmed, and G. Athanasopoulos, "Optimal combination forecasts for hierarchical time series," Monash University (2007), 1-21.
- Kahn, K. B., "Revisiting top-down versus bottom-up forecasting," The Journal of Business Forecasting (1998), 14-19.
- Liaw, A. A. and M. Wiener, "Classification and regression by random forest," R news 2, 3 (2002), 18-22.
- Miles, J. and M. Shevlin, Applying Regression and Correlation: a Guide for Students and Researchers, Sage publications, London, UK, 2001.
- Miller Jr., R. G., Beyond ANOVA, Basics of Applied Statistics, John Wiley and Sons, Inc., NY, USA, 1986.
- Moon, S., "The impact of demand features on the performance of hierarchical forecasting: case study for spare parts in the navy," Korean Management Science Review 29, 1 (2012), 101-114. https://doi.org/10.7737/KMSR.2012.29.1.101
- Moon, S., A. Simpson, and, C. Hicks, "The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy-A case study," International Journal of Production Economics 140 (2012a), 794-802. https://doi.org/10.1016/j.ijpe.2012.02.012
- Moon, S., A. Simpson, and C. Hicks, "The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand," International Journal of Production Economics (2012b), 10.1016/j.ijpe.2012.02.016.
- Ottenbacher, K. J., P. M. Smith, S. B. Illig, R. T. Linn, R. C. Fiedler, and C. V. Granger, "Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke," Journal of Clinical Epidemiology 54, 11 (2001), 1159-1165. https://doi.org/10.1016/S0895-4356(01)00395-X
- Perlich, C., F. Provost, and J. S. Simonoff, "Tree induction vs. logistic regression: a learning-curve analysis," The Journal of Machine Learning Research 4, (2003), 211-255.
- Prasad, A. M., L. R. Iverson, and A. Liaw, "Newer classification and regression tree techniques: bagging and random forests for ecological prediction," Ecosystems 9, 2 (2006), 181-199. https://doi.org/10.1007/s10021-005-0054-1
- Ripley, B., nnet: feed-forward neural networks and multinomial log-linear models, Available at: http://cran.ma.imperial.ac.uk/, Accessed on 4 June 2012.
- Schwarzkopf, A. B., R. J. Tersine, and J. S. Morris, "Top-down versus Bottom-up Forecasting Strategies," International Journal of Production Research 26, 11 (1988), 1833-1843. https://doi.org/10.1080/00207548808947995
- Seon, M.-S. and U, J.-U., "A study on forecasting of repair part demands of Korean Military: focused on Navy," The Korean Journal of Defense Analysis 85 (2009), 201-234.
- Shlifer, E. and R. W. Wolff, "Aggregation and proration in forecasting," Management Science 25, 6 (1979), 594-603. https://doi.org/10.1287/mnsc.25.6.594
- Steyerberg, E. W., F. E. Harrell, G. J. J. M. Borsboom, M. J. C. R. Eijkemans, Y. V. Vergouwe, and J. D. F. Habbema, "Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis," Journal of Clinical Epidemiology 54, 8 (2001), 774-781. https://doi.org/10.1016/S0895-4356(01)00341-9
- Thereau, T. M. and B. Atkinson, rpart: recursive partitioning, Available at: http://CRAN.R-project.org/package=rpart, Accessed on 23 January 2012.
- Tu, J. V., "Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes," Journal of Clinical Epidemiology 49, 11 (1996), 1225-1231. https://doi.org/10.1016/S0895-4356(96)00002-9
- Venables, W. N., B. D. Ripley, Modern Applied Statistics with S, 4th edn., Springer, NY, 2002.
- Widiarta, H., S. Viswanathan, and R. Piplani, "On the effectiveness of top-down strategy for forecasting autoregressive demands," Naval Research Logistics 54, 2 (2006), 176-188.
- Widiarta, H., S. Viswanathan, and R. Piplani, "Forecasting item-level demands: an analytical evaluation of top-down versus bottom-up forecasting in a production-planning framework," IMA Journal of Management Mathematics 19 (2008a), 207-218. https://doi.org/10.1093/imaman/dpm039
- Widiarta, H., S. Viswanthan, and R. Piplani, "Evaluation of hierarchical forecasting for substitutable products," International Journal of Services and Operations Management 4, 3 (2008b), 277-295. https://doi.org/10.1504/IJSOM.2008.017295
- Widiarta, H., S. Viswanathan, and R. Piplani, "Forecasting aggregate demand: an analytical evaluation of top-down versus bottom-up forecasting in a production planning framework," International Journal of Production Economics 118, 1 (2009), 87-94. https://doi.org/10.1016/j.ijpe.2008.08.013
- Wilks, D. S., Statistical Methods in the Atmospheric Science, 3rd edn., Elsevier, Oxford, UK, 2011.
- Williams, G., "Rattle: a data mining GUI for R," The R Journal 1, 2 (2009), 45-55.
- Williams, G., Data Mining with Rattle and R: the Art of Excavating Data for Knowledge Discovery, Springer, London, UK, 2011.