УКР ENG

Search:


Email:  
Password:  

 REGISTRATION CERTIFICATE

KV #19905-9705 PR dated 02.04.2013.

 FOUNDERS

RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS (KHARKIV, UKRAINE)

According to the decision No. 802 of the National Council of Television and Radio Broadcasting of Ukraine dated 14.03.2024, is registered as a subject in the field of print media.
ID R30-03156

 PUBLISHER

Liburkina L. M.

 SITE SECTIONS

Main page

Editorial staff

Editorial policy

Annotated catalogue (2011)

Annotated catalogue (2012)

Annotated catalogue (2013)

Annotated catalogue (2014)

Annotated catalogue (2015)

Annotated catalogue (2016)

Annotated catalogue (2017)

Annotated catalogue (2018)

Annotated catalogue (2019)

Annotated catalogue (2020)

Annotated catalogue (2021)

Annotated catalogue (2022)

Annotated catalogue (2023)

Annotated catalogue (2024)

Thematic sections of the journal

Proceedings of scientific conferences


Simulating and Forecasting Periodic Behavior of a Discrete Time Series Using the Moving Average Method
Dmytrenko O. V., Ivashchenko P. A.

Dmytrenko, Oleksandr V., and Ivashchenko, Peter A. (2019) “Simulating and Forecasting Periodic Behavior of a Discrete Time Series Using the Moving Average Method.” Business Inform 5:106–110.
https://doi.org/10.32983/2222-4459-2019-5-106-110

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
Downloads/views: 9

Download article (pdf) -

UDC 330.341

Abstract:
A new method for simulating and forecasting a discrete time series using the moving average method is proposed and substantiated. The idea of the method is to use the latus rectum as a quantitative estimator of the shape of a parabolic segment of a time series trend. Since the actual number of values of this indicator in terms of nature of its periodicity resembles an array of innovative buckets, there arose an idea of using the foci of parabolic segments which approximate these buckets. To build the forecasts, the simplest method of double moving averages of the third order was chosen. Using the moving average method, it is possible to obtain not only forecasts of points or intervals but also more complex ones, e.g., those of parabolic segments. The method of forecasting with the help of moving averages has provided an opportunity to give a generalizing picture of the changes in the process being studied, as demonstrated by the example of the indicator “Innovative Activity of Ukrainian Enterprises”. It has made it possible to predict the intensification of innovation in Ukraine for the period up to 2024.

Keywords: moving average, innovative activity, double moving average.

Fig.: 1. Tabl.: 2. Formulae: 1. Bibl.: 18.

Dmytrenko Oleksandr V. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Economic Theory and Economic Methods of Management, V. N. Karazin Kharkiv National University (4 Svobody Square, Kharkіv, 61022, Ukraine)
Email: [email protected]
Ivashchenko Peter A. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Statistics, Accounting and Auditing, V. N. Karazin Kharkiv National University (4 Svobody Square, Kharkіv, 61022, Ukraine)
Email: [email protected]

List of references in article

Naukova ta innovatsiina diialnist Ukrainy, 2017 rik [Scientific and innovation activity of Ukraine, 2017]. Kyiv: Derzhavna sluzhba statystyky Ukrainy, 2018.
Statystychnyi shchorichnyk Ukrainy za 2017 rik [Statistical Yearbook of Ukraine for 2017]. Kyiv: Derzhavna sluzhba statystyky Ukrainy, 2018.
“Statystychna informatsiia / Ekonomichna statystyka / Nauka, tekhnolohii ta innovatsiina diialnist (1990-2016) / Zahalnyi obsiah vytrat za napriamamy innovatsiinoi diialnosti“ [Statistical information / Economic statistics / Science, technology and innovation activities (1990-2016) / Total expenditures on innovation activities]. Derzhavna sluzhba statystyky Ukrainy. http//www.ukrstat.gov.ua/
Posokhov, I. M., Ivashchenko, P. O., and Ivanova, V. B. “Tsyklichnist innovatsiinoi aktyvnosti pidpryiemstv Ukrainy“ [Cycle of innovative activity of Ukrainian enterprises]. Statystychni metody ta informatsiini tekhnolohii analizu sotsialno-ekonomichnoho rozvytku. Khmelnytskyi: KhUUP, 2018. 44-48.
Travail, Luis Antonio de Santa-Eulalia, Neumann, D., and Klasen, J. “A Simulation-Based Innovation Forecasting Approach Combining the Bass Diffusion Model, the Discrete Choice Model and System Dynamics. An Application in the German Market for Electric Cars“. The Third International Conference on Advances in System Simulation SIMUL 2011. Barcelona, Spain, 2011. 81-87.
Lukashin, Yu. P., and Rakhlina, L. I. Sovremennyye napravleniya statisticheskogo analiza vzaimosvyazey i zavisimostey [Modern trends in statistical analysis of relationships and dependencies]. Moscow: IMEMO RAN, 2012.
Hrudtsyna, Yu. V. “Innovatsiina diialnist v Ukraini: analiz i prohnozuvannia“ [Innovation in Ukraine: Analysis and Forecasting]. Biznes Inform, no. 2 (2019): 78-84.
Svetunkov, I. S. “Kratkosrochnoye prognozirovaniye sotsialno-ekonomicheskikh protsessov s ispolzovaniyem modeli s korrektsiyey“ [Short-term forecasting of socio-economic processes using a model with correction]. Biznes Inform, no. 5 (1) (2011): 109-114.
Klebanova, T. S., and Rudachenko, O. O. “Prohnozuvannia pokaznykiv finansovoi diialnosti pidpryiemstva zhytlovo-komunalnoho hospodarstva za dopomohoiu adaptyvnykh modelei“ [Forecasting of indicators of financial activity of the enterprise of housing and communal services using adaptive models]. Biznes Inform, no. 1 (2015): 143-149.
Meek, C., Chickering, D. M., and Heckerman, D. “Autoregressive Tree Models for Time-Series Analysis“. Proceedings of the Second International SIAM Conference on Data Mining. Arlington, VA: SIAM, 2002. 229-244.
Chuchuyeva, I. A. “Model prognozirovaniya vremennykh ryadov po vyborke maksimalnogo podobiya“ [The time series prediction model for the maximum similarity sample]: dis. ... . kand. tekhn. nauk : 05.13.18, 2012.
Kulynych, O. I. “Vybir naikrashchoho statystychnoho rivniannia zalezhnosti“ [Choosing the best statistical dependence equation]. Statystychni metody ta informatsiini tekhnolohii analizu sotsialno-ekonomichnoho rozvytku. Khmelnytskyi: KhUUP, 2018. 4-7.
Kulynych, R. O. “Ekonomichni normatyvni ta prohnozni rozrakhunky metodom statystychnykh rivnian zalezhnostei“ [Economic normative and forecast calculations by the method of statistical equations of dependencies]. Statystychni metody ta informatsiini tekhnolohii analizu sotsialno-ekonomichnoho rozvytku. Khmelnytskyi: KhUUP, 2018. 10-15.
Ivashchenko, P. O., Hlushach, Yu. S., and Ivanova, V. B. “Kvaziadaptyvne prohnozuvannia innovatsiinoi diialnosti pidpryiemstv Ukrainy“ [Quasi-adaptive forecasting of innovation activity of enterprises of Ukraine]. Biznes Inform, no. 6 (2018): 180-185.
Davnis, V. V., and Tinyakova, V. I. Adaptivnyye modeli: analiz i prognoz v ekonomicheskikh sistemakh [Adaptive models: analysis and forecast in economic systems]. Voronezh: VGU, 2006.
Denisenko, V. I. Empiriometriya [Empiriometry]. Vladimir: Izd-vo VlGU, 2017.
Hurianova, L. S. et al. Ekonometryka [Econometrics]. Kharkiv: KhNEU im. S. Kuznetsia, 2015.
Khank, D. E., Uichern, D. U., and Rayte, A. D. Biznes-prognozirovaniye [Business forecasting]. Moscow: Vilyams, 2017.

 FOR AUTHORS

License Contract

Conditions of Publication

Article Requirements

Regulations on Peer-Reviewing

Publication Contract

Current Issue

Frequently asked questions

 INFORMATION

The Plan of Scientific Conferences


 OUR PARTNERS


Journal «The Problems of Economy»

  © Business Inform, 1992 - 2024 The site and its metadata are licensed under CC BY-SA. Write to webmaster