Method of autoregression in application of singular-spectral analysis of time series for forecasting production of oil and gas industry products

¹ The Federal Center of Expertize and Analyzis, 33, p. 4, Talalikhina str., Moscow, 109316, Russia
² Institute of Astronomy of the Russian Academy of Sciences, 48, Pyatnitskaya str., Moscow, 119017, Russia
³ Federal state unitary enterprise «All-Russia scientific and research institute «Center», 11, p.1., Sadovaya-Kudrinskaya str., Moscow, 123242, Russia
⁴ Marine Hydrophysical Institute, Russian Academy of Sciences, 2, Каpitanskaya str., Sevastopol, 299011, Russia
⁵ Reshetnev Siberian State University of Science and Technology, 31, Krasnoiarskii Rabochii Prospekt, Krasnoyarsk, 660037, Russia
⁶ Lavochkin Association, 24, Leningradskaya str., Khimki, Moscow region, 141402, Russia

^* Corresponding author: kartsan2003@mail.ru

Abstract

More and more time series data are produced in various fields. It provides data for the research of time series analysis method, and promotes the development of time series research. Due to the generation of highly complex and large-scale time series data, the construction of forecasting models for time series data brings greater challenges. The theoretical aspects of using the model of singular-spectral analysis of time series with the use of autoregression are considered, and the justification of the expediency of using this model for forecasting the production of products for both the oil and gas industry and dual-use products is given. Both autoregressive model and decision tree model can be applied with the same degree of reliability for forecasting aggregate values of production.