Uncertainties in data processing, forecasting and decision making
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2023.3.05Keywords:
mathematical model, statistical data uncertainties, system analysis principles, forecasting, decision support systemAbstract
Forecasting, dynamic planning, and current statistical data processing are defined as the process of estimating an enterprise’s current state on the market compared to other competing enterprises and determining further goals as well as sequences of actions and resources necessary for reaching the goals stated. In order to perform high-quality forecasting, it is proposed to identify and consider possible uncertainties associated with data and expert estimates. This is one of the system analysis principles to be hired for achieving high-quality final results. A review of some uncertainties is given, and an illustrative example showing improvement of the final result after considering possible stochastic uncertainty is provided.
References
R.S. Tsay, Analysis of financial time series. Chicago: Wiley & Sons, Ltd., 2010, 715 p.
L. Harris, X. Hong, and Q.Gan, Adaptive Modeling, Estimation and Fusion from Data. Berlin: Springer, 2002, 323 p.
P. Congdon, Applied Bayesian Modeling. Chichester: John Wiley & Sons, Ltd., 2003, 472 p.
S.M. DeLurgio, Forecasting Principles and Applications. Boston: McGraw-Hill, 1998, 802 p.
S.J. Taylor, “Modeling stochastic volatility: a review and comparative study,” Mathematical Finance, vol. 4, no. 2, pp. 183–204, 1994.
F. Burstein and C.W. Holsapple, Handbook of Decision Support Systems. Berlin: Springer-Verlag, 2008, 908 p.
C.W. Hollsapple and A.B. Winston, Decision Support Systems. Saint Paul (MN): West Publishing Company, 1996, 860 p.
P.I. Bidyuk, O.P. Gozhiy, Computer decision support systems. Mykolaiv: Petro Mohyla Black Sea National University, 2012, 380 p.
E. Xekalaki and S.Degiannakis, ARCH Models for Financial Applications. Chichester: Wiley & Sons, Inc., 2010, 550 p.
C. Chatfield, Time Series Forecasting. Boca Raton: Chapman & Hall/CRC, 2000, 267 p.
W.N. Anderson, G.B. Kleindorfer, P.R. Kleindorfer, and M.B. Woodroofe, “Consistent estimates of the parameters of a linear system,” The Annals of Mathematical Statistics, vol. 40, no. 6, pp. 2064–2075, 1969.
B.P. Gibbs, Advanced Kalman Filtering, Least-squares and Modeling. Hoboken (New Jersey): John Wiley & Sons, Inc., 2011, 627 p.
W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, Markov Chain Monte Carlo in Practice. New York: Chapman & Hall/CRC, 2000, 486 p.
F.V. Jensen and Th.D. Nielsen, Bayesian Networks and Decision Graphs. New York: Springer, 2007, 457 p.
M.Z. Zgurovsky, P.I. Bidyuk, O.M. Terentyev, and T.I. Prosyankina-Zharova, Bayesian Networks in Decision Support Systems. Kyiv: Edelweiss, 2015, 300 p.
J.M. Bernardo and A.F.M. Smith, Bayesian theory. New York: John Wiley & Sons, Ltd., 2000, 586 p.
W.M. Bolstad, Understanding Computational Bayesian Statistics. Hoboken (New Jersey): John Wiley & Sons, Ltd, 2010, 334 p.