Abstract
This article examines the oil consumption dynamics from 1965 to 2020 in the United States of America. The corresponding time series fluctuations and trend components analysis is carried out. The oil consumption dynamics a short-term forecast is also built. The trend is preliminarily eliminated based on the proportions’ theory for time series fluctuations further analysis. Near-period values are determined using shift and autocorrelation functions. Based on the near-period obtained value, a development cell is built. Presumably, in the period from 2021 to 2042, the oil consumption dynamics in the United States will be distinguished by steady, slow growth with an average annual rate of about 1%, which will be interrupted by short-term recessions. After 2042, an unstable development phase will begin until 2061. During this period, a qualitative restructuring and a change in the oil consumption dynamics structure in the United States will take place. #COMESYSO1120.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Veniaminov, S.S.: Revealing Latent Structure Laws in Processes and Signals. M., URSS p. 216 (2014)
Kuzmin, V.I., Gadzaov, A.F.: Method of construction of models on empirical data. The manual. M., MGTU MIREA 94 (2012)
Serebrennikov, M.G., Pervozvansky, A.A.: Revealing of the Latent Periodicity. M., Science p. 244 (1965)
Dzerjinsky, R.I., Pronina, E.N., Dzerzhinskaya, M.R.: The Structural analysis of the world gold prices dynamics. In: Silhavy, R. (ed.) CSOC 2020. AISC, vol. 1225, pp. 352–365. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51971-1_29
Paramonov A.A, Kuzmin V.I., Dzerjinsky R.I.: Analysis of almost-periodic and almost-proportional characteristics of a representative sample local minima time series. In: IOP Conference Series: Materials Science and Engineering, vol. 1047, p. 012045 (2020).https://doi.org/10.1088/1757-899X/1047/1/012045.
Semenenko, M.G., et al.: How to use neural network and web technologies in modeling complex technical systems. In: IOP Conference Series: Materials Science and Engineering, vol. 537, 032095 (2019)
Gruzenkin, D.V., Sukhanova, A.V., Novikov, O.S., Grishina, G.V., Rutskiy, V.N., Tsarev, R.Y., Zhigalov, K.Y.: Neural networks to solve modern artificial intelligence tasks. J. Phys. Conf. Series 1399, 033058 (2019)
Our world in data. https://ourworldindata.org/search?q=consumption+oil. Accessed 8 Aug 2020
Machinelearning. http://www.machinelearning.ru/. Accessed 8 Aug 2020
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Vysotskaya, A.A., Dzerzhinsky, R.I., Pronina, E.N. (2021). Oil Consumption Analysis in the USA in Current Time. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Application in Informatics. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-030-90318-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-90318-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90317-6
Online ISBN: 978-3-030-90318-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)