9786204190921 - Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks: A Deep Learning approach for forecasting stock price time-series data in groups von Lawi, Ar... (8 Ergebnisse)

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Taschenbuch. Zustand: Neu. Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks | A Deep Learning approach for forecasting stock price time-series data in groups | Armin Lawi (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204190921 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Stocks or shares are securities that confirm the participation or ownership of a person or entity in a company. Stocks are an attractive investment option because they can generate large profits compared to other businesses, however, the risk can also result in large losses in a short time. Thus, minimizing the risk of loss in stock buying and selling transactions is very crucial and important, and it requires careful attention to stock price movements. Technical factors are one of the methods that are used in learning the prediction of stock price movements through past historical data patterns on the stock market. Therefore, forecasting models using technical factors must be careful, thorough, and accurate, to reduce risk appropriately. This book presents the LSTM and GRU Neural Networks to build stock price forecasting models in groups using technical factors. The investigation uses seven years of benchmark time-series data on daily stock price movements with the same features as several previous related works to show differences in results. Time-series data on stock prices are grouped to follow the general pattern of stock price movements in the stock exchange market. 52 pp. Englisch.

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Zustand: New. PRINT ON DEMAND.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Lawi ArminArmin Lawi is an Associate Professor of Computer Science at Hasanuddin University, Indonesia where he obtained his Bachelor of Science (B.Sc.) in Mathematics. His Master of Engineering (M.Eng.) and Doctor of Engineering (Dr.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Stocks or shares are securities that confirm the participation or ownership of a person or entity in a company. Stocks are an attractive investment option because they can generate large profits compared to other businesses, however, the risk can also result in large losses in a short time. Thus, minimizing the risk of loss in stock buying and selling transactions is very crucial and important, and it requires careful attention to stock price movements. Technical factors are one of the methods that are used in learning the prediction of stock price movements through past historical data patterns on the stock market. Therefore, forecasting models using technical factors must be careful, thorough, and accurate, to reduce risk appropriately. This book presents the LSTM and GRU Neural Networks to build stock price forecasting models in groups using technical factors. The investigation uses seven years of benchmark time-series data on daily stock price movements with the same features as several previous related works to show differences in results. Time-series data on stock prices are grouped to follow the general pattern of stock price movements in the stock exchange market.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Stocks or shares are securities that confirm the participation or ownership of a person or entity in a company. Stocks are an attractive investment option because they can generate large profits compared to other businesses, however, the risk can also result in large losses in a short time. Thus, minimizing the risk of loss in stock buying and selling transactions is very crucial and important, and it requires careful attention to stock price movements. Technical factors are one of the methods that are used in learning the prediction of stock price movements through past historical data patterns on the stock market. Therefore, forecasting models using technical factors must be careful, thorough, and accurate, to reduce risk appropriately. This book presents the LSTM and GRU Neural Networks to build stock price forecasting models in groups using technical factors. The investigation uses seven years of benchmark time-series data on daily stock price movements with the same features as several previous related works to show differences in results. Time-series data on stock prices are grouped to follow the general pattern of stock price movements in the stock exchange market.