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16 Июня 2024

В Международном университете природы, общества и человека «Дубна» проведен детальный обзор исследований в области прогнозирования временных рядов.

26.04.2016

The recent surge in research of artificial neural networks (ANN) showed that neural networks have a strong capability in predicting and classification problems. ANN are successfully used to solve various problems in many areas of business, industry and science.

The rapid growth in the number of articles published in scientific journals in various disciplines shows high interest in neural networks. It suffices to consider several large databases to understand the huge number of articles about studying neural networks published during the year. There are thousands of articles.

Neural networks can be run in parallel with the input variables and therefore can simply handle large amounts of data. The main advantage of neural networks is the ability to find patterns. ANN are a promising alternative in the professional forecasting toolbox. In fact, the non-linear structure of neural networks is partially useful to identify complex relationships in most real-world problems.

Perhaps neural networks are the most universal method of forecasting considering the fact that they can not only find non-linear structures in problems, but they can also simulate the processes of linear processes. For example, the possibility of using neural networks in modeling a linear time-series line has been studied and confirmed by a number of researchers.

One of the main applications of IOS is forecasting. In recent years, the interest in forecasting using neural networks has increased. Forecasting has a long history, and its importance is reflected in the application in a variety of disciplines from business to engineering.

The ability to predict the future accurately is fundamental in many decision-making processes in planning, developing strategies, building policy, as well as in the management of supply and stock prices. As such, forecasting has always been an area to invest a lot of effort. It still remains an important and active area of human activity in the present and will continue to evolve in the future. A review of the research needs in prediction was presented by Armstrong.

For several decades forecasting has been dominated by linear methods, which are simple in design and use. They are also easy to understand and interpret. However, linear models have significant limitations, due to which they cannot discern any nonlinear relationships in data. Earlier in 1980 there was a large-scale competition in forecasting. Most widely used linear methods were tested on more than 1,000 real-time series 7]. It has been found that none of linear models showed the best results worldwide that can be interpreted as a failure of linear models in accounting with a certain degree of non-linearity that is common for the real world.

Подробное описание дается в статье «Обзор исследований по прогнозированию временных рядов на основе гибридных методов, нейронных сетей и множественной регрессии», авторы: Ярушев С.А., Аверкин А.Н.(Международный университет природы, общества и человека «Дубна», г. Дубна).