Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks

Language: English

Published by LAP LAMBERT Academic Publishing Jul 2021, 2021

620419092X / 9786204190921

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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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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.…

Seller Inventory # 9786204190921

Title
Accurately Forecasting Stock Prices using LSTM and GRU Neural Networks
Author
Armin Lawi
Publisher
LAP LAMBERT Academic Publishing Jul 2021
Publication year
2021
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
620419092X
ISBN 13
9786204190921
Item weight
96 grams
Dimensions
220x150x4 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

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BuchWeltWeit Ludwig Meier e.K.

Germany