Инд. авторы:	Chirikhin K.S., Ryabko B.Y.
Заглавие:	Application of arti cial intelligence and data compression methods to time series forecasting
Библ. ссылка:	Chirikhin K.S., Ryabko B.Y. Application of arti cial intelligence and data compression methods to time series forecasting // Applied Methods of Statistical Analysis. Statistical Computation and Simulation - AMSA'2019 (Novosibirsk, Russia, 18.09-20.09.2019): Proceedings of the International Workshop / Editors: Boris Lemeshko, Mikhail Nikulin, Narayanaswamy Balakrishnan. - 2019. - Novosibirsk. - P.553-560.
Внешние системы:	РИНЦ: 41186694;
Реферат:	eng: We consider the problem of time-series forecasting. Nowadays, widespread classical methods can successfully nd periodic patterns and simple types of trends. However, in real-life data there may be more complex regularities. For example, time series of economic indicators can have quite complex patterns, because their dynamics can be inuenced by participants, who, in turn, can use very complex strategies. In this paper we, rst, show how it is possible to combine several methods of forecasting into a single one in such a way that, asymptotically, the accuracy of the obtained method will be equal to the accuracy of the best method among the combined ones. Secondly, we use this approach to combine several methods of forecasting, including "classical" data compression algorithms, a method based on sensing multihead nite automata and a method that uses grammar-based codes. The results of computational experiments are presented.
Ключевые слова:	arti cial intelligence; time series forecasting; time series models; universal coding;
Издано:	2019
Физ. характеристика:	с.553-560
Конференция:	Название: International Workshop "Applied Methods of Statistical Analysis. Statistical Computation and Simulation" Аббревиатура: AMSA'2019 Город: Novosibirsk Страна: Russia Даты проведения: 2019-09-18 - 2019-09-20 Ссылка: http://www.amsa.conf.nstu.ru/amsa2019/
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