Formation of prognostic software support for strategic decision-making in an organization

Authors

DOI:

https://doi.org/10.15587/1729-4061.2016.66306

Keywords:

prognostic software, managerial decision-making support, prediction/forecasting, integrated forecasting / aggregation of prognostic estimates

Abstract

The study suggests a four-level model of a prognostic software system designed to solve the problems set forth for prognostic management of strategic decision-making support, including collection of statistical data, formation of a set of the main predictive methods, aggregation of prognostic estimates from different sources, and provision of an interactive mode of a parameter setting.

One of the models considered for the low level is the Brown prognostic model. A method of its parameter setting is suggested in the study on the basis of a retrospective analysis, which, unlike the existing ones, allows determining the tuning parameters of the model and ensures a maximum resistance of prognostic estimates to changes in the internal model parameters.

To create a means of prognostic data integration at the upper level, the study suggests a method of dynamic aggregation of prognostic estimates based on identifying prediction accuracy tendencies of alternative prediction sources, which, unlike the existing methods, ensures adaptability of the integration system and prognostic software support for strategic decision-making.

Author Biographies

Yuri Romanenkov, M. E. Zhukovsky National Aerospace University "Kharkiv Aviation Institute" 17 Chkalova str., Kharkiv, Ukraine, 61070

PhD, Associate Professor

Department of management 

Vasily Vartanian, M. E. Zhukovsky National Aerospace University "Kharkiv Aviation Institute" 17 Chkalova str., Kharkiv, Ukraine, 61070

Doctor of Technical Sciences, Professor

Department of management 

References

  1. Ledenev, A. Infografika: gde zarabatyvajut samolety s ukrainskoj propiskoj. Available at: http://forbes.net.ua/business/1411400-infografika-gde-zarabatyvayut-samolety-s-ukrainskoj-propiskoj
  2. Frenkel', A. A. (2007). Prognozirovanie proizvoditel'nosti truda: metody i modeli. Moscow: Jekonomika, 221.
  3. Kantorovich, G. G. (2002). Analiz vremennyh rjadov. Jekonomicheskij zhurnal VShJe, 1-2.
  4. Anderson, T. (1976). Statisticheskij analiz vremennyh rjadov. Moscow: Mir, 757.
  5. Kendjel, M. (1981). Vremennye rjady. Moscow: Finansy i statistika, 199.
  6. Boks, Dzh., Dzhenkins, G.; Pisarenko, V. F. (Ed.) (1974). Analiz vremennyh rjadov. Prognoz i upravlenie. Moscow: Mir, 406.
  7. Nosko, V. P. (2002). Jekonometrika. Vvedenie v regressionnyj analiz vremennyh rjadov. Moscow: NFPK, 273.
  8. Bidjuk, P. I., Fedorov, A. V. (2009). Pobudova systemy adaptyvnogo prognozuvannja finansovo-ekonomichnyh procesiv ta i'i' zastosuvannja. Naukovi praci: Ser. Komp’juterni tehnologii', 117 (104), 119–129.
  9. Solovjova, M. I. (2009). Z istorii' rozvytku koncepcii' systemnogo planuvannja i prognozuvannja. Nauka j ekonomika, 2 (4 (16)), 198–204.
  10. Svetun'kov, S. G., Svetun'kov, I. S. (2009). Metody social'no-jekonomicheskogo prognozirovanija. Vol. I. SPb.: Izd-vo SPbGUJeF, 147.
  11. Tihonov, E. Je. (2006). Metody prognozirovanija v uslovijah rynka. Nevinnomyssk, 221.
  12. Mironov, E. A. Upravlencheskij cikl kak tehnologija sotrudnichestva. Available at: http://www.coverdale.ru/pdf/article2.pdf
  13. Pastovens'kyj, O. V. (2012). Osoblyvosti upravlins'kogo cyklu v umovah rozvytku gromads'ko-derzhavnogo upravlinnja zagal'noju seredn'oju osvitoju. Naukovi zapysky Ternopil's'kogo nacional'nogo pedagogichnogo universytetu im. Volodymyra Gnatjuka. Ser. Pedagogika, 2, 128–133.
  14. Afanas'eva, T. V. (2013). Modelirovanie nechetkih tendencij vremennyh rjadov. Ul'janovsk: UlGTU, 215.
  15. Batyrshin, I. Z., Sheremetov, L. B. (2007). Modeli i metody perceptivnogo data majninga vremennyh rjadov dlja sistem podderzhki prinjatija reshenij. Nechetkie sistemy i mjagkie vychislenija, 2 (1), 152–161.
  16. Kovalerchuk, B., Vityaev, E. (2000). Data Mining in Finance: Advances in Relational and Hybrid methods. Kluwer Academic Publishers, 308.
  17. Aleksandrov, F. I., Goljandina, N. Je. Avtomatizacija vydelenija trendovyh i periodicheskih sostavljajushhih vremennogo rjada v ramkah metoda «Gusenica»-SSA. Available at: http://www.pdmi.ras.ru/~theo/autossa/files/Exponenta.Pro--paper--AutoSSA.pdf
  18. Chen, K.-Y. (2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, 38 (8), 10368–10376. doi: 10.1016/j.eswa.2011.02.049
  19. Matijaš, M., Suykens, J. A. K., Krajcar, S. (2013). Load forecasting using a multivariate meta-learning system. Expert Systems with Applications, 40 (11), 4427–4437. doi: 10.1016/j.eswa.2013.01.047
  20. Jansen, W. J., Jin, X., de Winter, J. M. (2016). Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts. International Journal of Forecasting, 32 (2), 411–436. doi: 10.1016/j.ijforecast.2015.05.008
  21. Kashheeva, V. Ju. (2013). Informacionnaja tehnologija analiza mnogokomponentnyh processov po vremennym rjadam na osnove interval'nyh prognoznyh modelej. Sistemi upravlіnnja, navіgacіi ta zv’jazku, 3 (27), 128–133.
  22. Romanenkov, Yu. (2015). Analysis of the predictive properties of Brown's model in the extended domain of the internal parameter. MOTROL. Commission of Motorization and Energetics in Agriculture, 17 (8), 27–34.
  23. Romanenkov, Ju. A., Zejniev, T. G. (2014). Metod parametricheskogo sinteza modeli Brauna na osnove retrospektivnoj mnogokriterial'noj optimizacii. Galuzeve mashinobuduvannja, budіvnictvo, 2(41), 48–56.
  24. Romanenkov, Ju. A. (2014). Parametricheskij analiz oblasti adekvatnosti adaptivnoj prognoznoj modeli Brauna. Naukovі pracі Pіvdennogo fіlіalu Nacіonal'nogo unіversitetu bіoresursіv і prirodokoristuvannja Ukraini «Krims'kij agrotehnologіchnij unіversitet», 162, 228–236.
  25. Goljandina, N. Je. (2004). Metod «Gusenica»-SSA: prognoz vremennyh rjadov. SPb: Izd-vo SPbGU, 52.
  26. Vautard, R., Ghil, M. (1989). Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series. Physica D: Nonlinear Phenomena, 35 (3), 395–424. doi: 10.1016/0167-2789(89)90077-8
  27. Yiou, P., Baert, E., Loutre, M. F. (1996). Spectral analysis of climate data. Surveys in Geophysics, 17 (6), 619–663. doi: 10.1007/bf01931784
  28. Lisi, F., Nicolis, O., Sandri, M. (1995). Combination of singular spectrum analysis and auto regressive model for short term load forecasting. Neural Process Lett., 2 (4), 6–10.
  29. Hassani, H., Heravi, S., Zhigljavsky, A. (2009). Forecasting European industrial production with singular spectrum analysis. International Journal of Forecasting, 25 (1), 103–118. doi: 10.1016/j.ijforecast.2008.09.007
  30. Dai, W., Lu, C.-J. (2008). Financial Time Series Forecasting Using A Compound Model Based on Wavelet Frame and Support Vector Regression. 2008 Fourth International Conference on Natural Computation, 328–332. doi: 10.1109/icnc.2008.455
  31. Lukashin, Ju. P. (2003). Adaptivnye metody kratkosrochnogo prognozirovanija vremennyh rjadov. Moscow: Finansy i statistika, 416.
  32. Berzlev, A. Ju., Maljar, M. M., Nikolenko, V. V. (2011). Adaptivnye kombinirovannye modeli prognozirovanija birzhevyh pokazatelej. Vestnik Cherkasskogo gos. tehnolog. un-ta. Serija: tehnicheskie nauki, 1, 50–54.
  33. Berzlev, A. Ju., Maljar, N. N., Nikolenko, V. V. (2011). Metody prognozirovanija dlja prinjatija jeffektivnyh reshenij v mnogourovnevyh modeljah. Nauch. vestnik Uzhgorod. un-ta. Serija matem. i informatika, 22, 18–25.
  34. Kurbatskii, V. G., Sidorov, D. N., Spiryaev, V. A., Tomin, N. V. (2011). On the neural network approach for forecasting of nonstationary time series on the basis of the Hilbert-Huang transform. Automation and Remote Control, 72 (7), 1405–1414. doi: 10.1134/s0005117911070083
  35. Zhang, W. Q., Xu, C. (2011). Time series forecasting method based on Huang transform and BP neural network. 2011 Seventh International Conference on Computational Intelligence and Security, 497–502. doi: 10.1109/cis.2011.116
  36. Lu, C.-J., Wu, J.-Yu, Lee, T.-S. (2009). ICA-Based Signal Reconstruction Scheme with Neural Network in Time Series Forecasting. 2009 First Asian Conference on Intelligent Information and Database Systems, 318–323. doi: 10.1109/aciids.2009.28
  37. Xiang, L., Zhu, Y., Tang, G.-J. (2009). A hybrid support vector regression for time series forecasting. 2009 WRI World Congress on Software Engineering, 161–165. doi: 10.1109/wcse.2009.130
  38. Sallehuddin, R., Shamsuddin, S. M., Hashim, S. Z. M. (2008). Hybridization Model of Linear and Nonlinear Time Series Data for Forecasting. 2008 Second Asia International Conference on Modelling & Simulation (AMS), 597–602. doi: 10.1109/ams.2008.142
  39. Shhelkalin, V. N. (2014). “Caterpillar”-SSA and Box-Jenkins hybrid models and methods for time series forecasting. Eastern-European Journal of Enterprise Technologies, 5/4 (71), 43–62. doi: 10.15587/1729-4061.2014.28172
  40. Vartanjan, V. M., Romanenkov, Ju. A., Kashheeva, V. Ju. (2011). Evaluation of the frequency parameters of the model-Theil Veydzh in problems of short-term forecasting. Eastern-European Journal of Enterprise Technologies, 1/5 (49), 49–53. Available at: http://journals.uran.ua/eejet/article/view/2362/2164
  41. Romanenkov, Ju. A., Vartanjan, V. M., Revenko, D. S. (2014). Kompleksirovanie prognoznyh ocenok v sisteme monitoringa pokazatelej sostojanija biznes-processa. Sistemi upravlіnnja, navіgacіi ta zv’jazku, 2 (30), 79–86.
  42. Bidjuk, P. I., Gasanov, A. S., Vavilov, S. E. (2013). Analiz kachestva ocenok prognozov s ispol'zovaniem metoda kompleksirovanija. Sistemnі doslіdzhennja ta іnformacіjnі tehnologіi, 4, 7–16.
  43. Sineglazov, V. M., Chumachenko, E. I., Gorbatjuk, V. S. (2012). Metod reshenija zadachi prognozirovanija na osnove kompleksirovanija ocenok. Іnduktivne modeljuvannja skladnih sistem, 4, 214–223.
  44. Vasil'ev, A. A. (2015). Ob’edinenie prognozov jekonomicheskih pokazatelej na osnove bives-ocenki s vesovoj funkciej H'jubera. Aktual'nye problemy gumanitarnyh i estestvennyh nauk, 10-4. Available at: http://cyberleninka.ru/article/n/obedinenie-prognozov-ekonomicheskih-pokazateley-na-osnove-bives-otsenki-s-vesovoy-funktsiey-hyubera (Last accessed: 23.03.2016).
  45. Molev, M. D., Zanina, I. A., Stuzhenko, N. I. (2013). Sintez prognoznoj informacii v praktike ocenki jekologo-jekonomicheskogo razvitija regiona. Inzhenernyj vestnik Dona, 4. Available at: http://www.ivdon.ru/magazine/archive/n4y2013/1993
  46. Romanenkov, Ju. A., Vartanjan, V. M.; Savchuk, L. M. (Ed.) (2014). Sintez intellektual'nyh prognoznyh kompleksov v konturah upravlenija social'no-jekonomicheskimi sistemami. U kn.: Sistemi prijnjattja rіshen' v ekonomіcі, tehnіcі ta organіzacіjnih sferah: vіd teorіi do praktiki. Pavlograd: ART Sintez-T, 372–379.
  47. Demidova, L. A., Skvorcova, T. S. (2011). Programmnyj kompleks prognozirovanija znachenij vremennyh rjadov s ispol'zovaniem gibridnyh tehnologij. Matematicheskoe i programmnoe obespechenie vychislitel'nyh sistem. Rjazanskij gosudarstvennyj radiotehnicheskij universitet, 57–61.
  48. Shhavelev, L. B. (1998). Sposoby analiticheskoj obrabotki dannyh dlja podderzhki prinjatija reshenij. SUBD, 4-5, 51–60.
  49. Brown, R. G. (1963). Smoothing forecasting and prediction of discrete time series. – N. Y., 480.
  50. Revenko, D. S., Vartanjan, V. M., Romanenkov, Ju. O., Art'omova, A. V. (2015). Komp’juterna programa «Korotkostrokove prognozuvannja makroekonomichnyh procesiv v umovah interval'noi' nevyznachenosti». Svidoctvo pro rejestraciju avtors'kogo prava na tvir № 58201 vid 21.01.2015.
  51. Romanenkov, Ju. O., Vartanjan, V. M., Revenko, D. S. (2015). Komp’juterna programa «Dynamychne kompleksuvannja prognoznyh ocinok v systemi upravlinnja vyrobnycho-ekonomichnymy procesamy na pidpryjemstvi». Svidoctvo pro rejestraciju avtors'kogo prava na tvir № 58203 vid 21.01.2015.

Downloads

Published

2016-04-27

How to Cite

Romanenkov, Y., & Vartanian, V. (2016). Formation of prognostic software support for strategic decision-making in an organization. Eastern-European Journal of Enterprise Technologies, 2(9(80), 25–34. https://doi.org/10.15587/1729-4061.2016.66306

Issue

Vol. 2 No. 9(80) (2016): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2016 Vasily Vartanian, Yuri Romanenkov

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.