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A data mining approach to improve military demand forecasting

Thiagarajan R., Rahman M., Gossink N., Calbert G.

Journal of Artificial Intelligence and Soft Computing Research

2014

Vol. 4, No. 3

205--214

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.

Effective number of observations and unbiased estimators of variance for autocorrelated data - an overview

Zięba A.

Metrology and Measurement Systems

2010

Vol. 17, nr 1

3-16

When observations are autocorrelated, standard formulae for the estimators of variance, s², and variance of the mean, s²(x), are no longer adequate. They should be replaced by suitably defined estimators, sa² and sa²(x), which are unbiased given that the autocorrelation function is known. The formula for sa² was given by Bayley and Hammersley in 1946, this work provides its simple derivation. The quantity named effective number of observations neff is thoroughly discussed. It replaces the real number of observations n when describing the relationship between the variance and variance of the mean, and can be used to express sa² and sa²(x) in a simple manner. The dispersion of both estimators depends on another effective number called the effective degrees of freedom veff. Most of the formulae discussed in this paper are scattered throughout the literature and not very well known, this work aims to promote their more widespread use. The presented algorithms represent a natural extension of the GUM formulation of type-A uncertainty for the case of autocorrelated observations.