Forecasting Stock Price using LSTM-CNN Method
Dinesh Reddy1, Abhinav Karthik2

1Dinesh Reddy*, Department of Computer Science Engineering, SRM Institute of Science and Technology, Kattankulathur, India.
2Abhinav Karthik, Department of Computer Science Engineering, Manipal Institute of Technology, Manipal, India
Manuscript received on August 30, 2021. | Revised Manuscript received on September 09, 2021. | Manuscript published on October 30, 2021. | PP: 01-08 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A31171011121 | DOI: 10.35940/ijeat.A3117.10110121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information. Our LSTM-CNN combination highlight model surpasses single models in foreseeing evaluating. Also, we track down that the candle graph is the most precise image of a stock diagram that you can use to anticipate costs. Subsequently, this examination shows that prescient mistake can be viably decreased by utilizing a blend of transitory and picture components from similar information as opposed to utilizing these provisions independently.
Keywords: LSTM, CNN, RNN, STOCK MARKET.