Forecasting Development of Economic Processes using Adapted Nonlinear Dynamics Methods
Alfira Kumratova1, Elena Popova2, Lilija Temirova3, Olga Shaposhnikova4
1Alfira Kumratova*, Department, Name of the Affiliated College or University, Industry, City, Country.
2Elena Popova, Department, Name of the Affiliated College or University, Industry, City, Country.
3Lilija Temirova, Department of Mathematics, North-Caucasian State Academy, Cherkessk, Russia.
4Olga Shaposhnikova, Department of Mathematics, North-Caucasian State Academy, Cherkessk, Russia.
Manuscript received on September 16, 2019. | Revised Manuscript received on October 05, 2019. | Manuscript published on October 30, 2019. | PP: 3082-3085 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1671109119/2019©BEIESP | DOI: 10.35940/ijeat.A1671.109119
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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: In this work authors propose using adapted nonlinear dynamics methods to prepare time series data for the forecast procedure in order to identify chaotic dynamics and to select forecast methods and models. Each step of the proposed set of methods for data preprocessing allows us to put forward proposals on certain properties of the studied time series. This, in turn, proves that to obtain reliable and reasonable conclusions about the type of behavior of the investigated system, the results of one of the many existing tests are not enough. Conducting a comprehensive analysis, will most correctly determine the type of behavior of the time series and its characteristics, which will make it possible to obtain a reliable forecast in the future.
Keywords: Non-linear trend, linear trend, Visualization, Gilmore test, Pseudophase space, Attractor.