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A data mining approach to improve military demand forecasting

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly autocorrelated models, have been adopted in the military planning process because a large number of stocks in the military inventory do not have consumption and usage rates per platform (e.g., ship). However, if an impending military operation is (significantly) different from prior campaigns then these prediction models may under or over estimate the demand of critical stocks leading to undesired operational impacts. To address this, we propose an approach to improve the accuracy of demand predictions by combining autocorrelated predictions with cross-correlated demands of items having known per-platform usage rates. We adopt a data mining approach using sequence rule mining to automatically determine crosscorrelated demands by assessing frequently co-occurring usage patterns. Our experiments using a military operational planning system indicate a considerable reduction in the prediction errors across several categories of military supplies.
Rocznik
Strony
205--214
Opis fizyczny
Bibliogr. 17 poz., rys.
Twórcy
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia
autor
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia
autor
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia
autor
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia
Bibliografia
  • [1] R. Thiagarajan, M. Rahman, G. Calbert, and D. Gossink, “Improving military demand forecasting using sequence rules,” in 6th Asian Conference on Intelligent Information and Database Systems (ACIIDS), pp. 475–484, 2014.
  • [2] S. Benjaafar, W. L. Cooper, and S. Mardan, “Production-inventory systems with imperfect advance demand information and updating,” Naval Research Logistics (NRL), vol. 58, no. 2, pp. 88–106, 2011.
  • [3] F. Karaesmen, “Value of advance demand information in production and inventory systems with shared resources,” in Handbook of Stochastic Models and Analysis of Manufacturing System Operations, vol. 192, pp. 139–165, 2013.
  • [4] R. Thiagarajan, M. A. Mekhtiev, G. Calbert, N. Jeremic, and D. Gossink, “Using military operational planning system data to drive reserve stocking decisions,” in 29th IEEE International Conference on Data Engineering (ICDE) Workshops, pp. 153–162, 2013.
  • [5] E. Gardner, “Exponential smoothing: The state of the art Part II,” International Journal of Forecasting, vol. 22, no. 4, pp. 637–666, 2006.
  • [6] G. Box, G. Jenkins, and G. Reinsel, Time Series Analysis: Forecasting and Control. 2008.
  • [7] M. Downing, M. Chipulu, U. Ojiako, and D. Kaparis, “Forecasting in airforce supply chains,” International Journal of Logistics Management, vol. 22, no. 1, pp. 127–144, 2011.
  • [8] R. Agrawal, T. Imieli´nski, and A. Swami, “Mining association rules between sets of items in large databases,” SIGMOD Rec., vol. 22, no. 2, pp. 207–216, 1993.
  • [9] H. Xiong, W. Zhou, M. Brodie, and S. Ma, “Topk correlation computation,” INFORMS Journal on Computing, vol. 20, no. 4, pp. 539–552, 2008.
  • [10] P. Fournier-Viger and V. S. Tseng, “TNS: mining top-k non-redundant sequential rules,” in ACM Symposium on Applied Computing (SAC), pp. 164–166, 2013.
  • [11] L.Wilkinson, “Tests of significance in stepwise regression,” Psychological Bulletin, vol. 86, no. 1, pp. 168–174, 1979.
  • [12] R. H. Myers, Classical and modern regression with applications, vol. 2. 1990.
  • [13] A. V. Oppenheim and R.W. Schafer, Discrete-Time Signal Processing. Prentice–Hall, 1989.
  • [14] P. Fournier-Viger, A. Gomariz, A. Soltani, and T. Gueniche, “SPMF: Open-Source Data Mining Platform - http://www.philippe-fournierviger. com/spmf/,” 2013.
  • [15] A. Zeileis, dynlm: Dynamic Linear Regression, 2013. R package version 0.3-2.
  • [16] R. J. Hyndman, G. Athanasopoulos, S. Razbash, D. Schmidt, Z. Zhou, Y. Khan, and C. Bergmeir, forecast: Forecasting functions for time series and linear models, 2013. R package version 4.06.
  • [17] C. J. Willmott, “On the validation of models,” Physical Geography, vol. 2, no. 2, pp. 184–194, 1981.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1ac4dafb-fa1f-41db-aa7e-e6376bf77bb6
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