Combination of deep-learning models to forecast stock price of AAPL and TSLA
Deep Learning is a promising domain. It has different applications in different areas of life and its application on the stock market is widely used due to its efficiency. Long Short Term Memory (LSTM) proved its efficiency in dealing with time-series data due to the unique hidden unit structure. Th...
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Veröffentlicht in: | Jordanian Journal of Computers and Information Technology 2022-12, Vol.8 (4), p.345-356 |
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description | Deep Learning is a promising domain. It has different applications in different areas of life and its application on the stock market is widely used due to its efficiency. Long Short Term Memory (LSTM) proved its efficiency in dealing with time-series data due to the unique hidden unit structure. This paper integrated LSTM with attention mechanism and sentiment analysis to forecast the closing price of two stocks; namely, APPL and TSLA, from the NASDAQ stock market. We compared our hybrid model with LSTM, LSTM with sentiment analysis and LSTM with Attention Mechanism. Three benchmarks were used to measure the performance of the models; the first one is Mean Square Error (MSE), the second one is Root Mean Square Error (RMSE) and the third one is Mean Absolute Error (MAE). The results show that the hybridization is more accurate than the LSTM model alone. |
doi_str_mv | 10.5455/jjcit.71-1655723854 |
format | Article |
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The results show that the hybridization is more accurate than the LSTM model alone.</description><identifier>ISSN: 2413-9351</identifier><identifier>EISSN: 2415-1076</identifier><identifier>DOI: 10.5455/jjcit.71-1655723854</identifier><language>ara ; eng</language><publisher>Amman, Jordan: Princess Sumaya University for Technology</publisher><subject>Artificial intelligence ; attention mechanism ; Business models ; Deep learning ; Discriminant analysis ; Efficiency ; hybrid model ; Information technology ; Investments ; lstm ; Mean square errors ; Natural language processing ; Neural networks ; Performance evaluation ; Prices ; Securities markets ; Sentiment analysis ; Stock exchanges ; Voice recognition ; Volatility ; Wavelet transforms</subject><ispartof>Jordanian Journal of Computers and Information Technology, 2022-12, Vol.8 (4), p.345-356</ispartof><rights>2022. 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subjects | Artificial intelligence attention mechanism Business models Deep learning Discriminant analysis Efficiency hybrid model Information technology Investments lstm Mean square errors Natural language processing Neural networks Performance evaluation Prices Securities markets Sentiment analysis Stock exchanges Voice recognition Volatility Wavelet transforms |
title | Combination of deep-learning models to forecast stock price of AAPL and TSLA |
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