- Tytuł:
- A data mining approach to improve military demand forecasting
- Autorzy:
-
Thiagarajan, R.
Rahman, M.
Gossink, N.
Calbert, G. - Powiązania:
- https://bibliotekanauki.pl/articles/91684.pdf
- Data publikacji:
- 2014
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
critical stocks
demand
forecasting
military operation
military planning
military supplies
autocorrelated
cross-correlated
data mining - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 3; 205-214
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł