Authors:
,,,
Branch:
Computer engineering
Volume:
VOLUME - 15
Album:
Issue-5, May-2021
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Abstract—Finding patterns in high dimensional data can be difficult because it cannot be easily visualized. Many different machine learning methods are able to fit this high dimensional data in order to predict and classify future data but there is typically a large expense on having the machine learn the fit for a certain part of the dataset. This paper proposes a deep learning way of defining different patterns in stock market prices. Using a CNN, the pattern is found within stock market data and predictions are made from it. The stock pattern is divided in five parts Decline in value of stock (Abrupt decline, smooth decline), incline in stock value (abrupt increase, smooth increase) and stable price.
Keywords—stock pattern recognition, CNN,OHLC
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Marc Velay and Fabrice Daniel, “Stock Chart Pattern recognition with Deep Learning”, Research gate, June 2018
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Victor Skuratov, Konstantin Kuzmin, Igor Nelin, Mikhail Sedankin, “Application of a convolution neural network to create a detector of technical analysis figures on exchange quotes charts”, (2019), «EUREKA: Physics and Engineering» Number 6 DOI: 10.21303/2461-4262.2019.001055
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Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction Hyejung Chung and Kyung-shik Shin Sustainability 2018, 10, 3765; doi:10.3390/su10103765
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An optimized CNN based robust sentiment analysis system on big social data using text polarity feature KomalpreetKaur, ChitenderKaur, TarandeepKaur Bhatia International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-6, April 2019
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Performance Comparison of Machine Learning Methods for Solving Handwriting Character Recognition Problem S.G.KIVANÇ1, A.E. BAKTIR2 and B.SEN3 International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES’18), May 11-13, 2018 Safranbolu, Turkey
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JSRD - International Journal for Scientific Research & Development| Vol. 6, Issue 07, 2018 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 408 Stock Market Prediction using RFR, DTR & SVR Ravikant1 Suman Kumar Swarnkar2L. P. Bhaiya
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International Journal of Computer Sciences and Engineering Open Access Research Paper Vol.-6, Issue-5, May 2018 E-ISSN: 2347-2693 Stock Market Analysis and Prediction using Hadoop and Machine Learning Piyush Jain, KaustubhBhat, HarshalKesharwani3 , PriteshBhate, Khushboo P Khurana
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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4842 Stock Market Forecasting Techniques: A Survey Rashmi Sutkatti1, Dr. D. A. Torse
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Xingyu Zhou, Zhisong Pan, Guyu Hu, Siqi Tang, Cheng Zhao “Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets”, Mathematical Problems in Engineering Volume 2018, Article ID 4907423, 11 pages
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