Motion perception with recurrent self-organizing maps based models

Baier, Volker

doi:10.1109/ijcnn.2005.1556021

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Therefore last years for forecasting financial processes at stock exchanges intelligent methods are widely used. One class of such methods are recurrent neural networks (RNN) [1][2][3][4][5][6][7]. They enable to detect hidden dependences in data and perform long-term forecast of time series.…”

Section: Introductionmentioning

confidence: 99%

“…They enable to detect hidden dependences in data and perform long-term forecast of time series. Now this class of RNN includes simple recurrent networks, LSTM and GRU [1][2][3][4][5][6][7][8][9][10]. As alternative intelligent method GMDH from the other side is also widely used for forecasting share prices at stock exchanges [11; 12] and other financial processes.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of computational intelligence methods in forecasting problems at stock exchanges

Zaychenko

Hamidov²,

Gasanov

2021

SRIT

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In this paper, the forecasting problem of share prices at the New York Stock Exchange (NYSE) was considered and investigated. For its solution the alternative methods of computational intelligence were suggested and investigated: LSTM networks, GRU, simple recurrent neural networks (RNN) and Group Method of Data Handling (GMDH). The experimental investigations of intelligent methods for the problem of CISCO share prices were carried out and the efficiency of forecasting methods was estimated and compared. It was established that method GMDH had the best forecasting accuracy compared to other methods in the problem of share prices forecasting.

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