Stationary Time Series in Pricing
Anna Anatolyevna Burdina1, Anna Aleksandrovna Nekhrest2, Yuriy Nikolayevich Frolov3, Yelena Timofeyevna Manayenkova4
1Anna Anatolyevna Burdina, Moscow Aviation Institute (State University of AerospaceTechnologies), Moscow, Russia.
2Anna Aleksandrovna Nekhrest, Moscow Aviation Institute (State University of AerospaceTechnologies), Moscow, Russia.
3Yuriy Nikolayevich Frolov, Moscow Aviation Institute (State University of AerospaceTechnologies), Moscow, Russia.
4Yelena Timofeyevna Manayenkova, Moscow Aviation Institute (State University of AerospaceTechnologies), Moscow, Russia.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2268-2272 | Volume-8 Issue-10, August 2019 | Retrieval Number: J11290881019/2019©BEIESP | DOI: 10.35940/ijitee.J1129.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The methods for analyzing the dynamics of time series are compared in this study, the mechanisms for assessing the accuracy of value forecasting are examined, and a brief description of the models and examples of their use are provided. The problem of choosing the optimal model according to the criterion of the minimum forecasting error is stated and solved. The methods of mathematical modeling, mathematical statistics and econometrics, such as autoregression, moving average, exponential smoothing, and neural network modeling were used to solve this problem. The result of the study is the algorithm for finding the optimal model based on minimizing the forecasting error, as well as the program that implements this algorithm.
Keywords: ARIMA, autoregression, dynamics analysis, exponential smoothing, forecast estimation, moving average, neural networks, pricing, time series.
Scope of the Article: Aerospace Engineering