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Huynh, Kyungbok Min And Hyeonjoon Moon,&quot; Deep Learning is Approach for the Short-Term Stock Trends Prediction based on Two Stream Gated Recurrent Unit Network&quot;, DOI 10.1109/ACCESS.2018.2868970, IEEE Access</unstructured_citation></citation><citation key="ref4"><unstructured_citation>Lei Shi, Zhiyang Teng, Le Wang, Yue Zhang, and Alexander Binder, &quot;DeepClue: Visual Interpretation of Text-based Deep Stock Prediction&quot;, DOI 10.1109/TKDE.2018.2854193, IEEE Transactions on Knowledge and the Data Engineering.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>Guang Liu And Xiaojie Wang, &quot;A Numerical-based Attention Method for Stock Market Prediction with Dual Information&quot;,10.1109/ACCESS.2018.2886367, IEEE Access</unstructured_citation></citation><citation key="ref6"><unstructured_citation>Rashmi Sutkatti, Dr. D. A. Torse, &quot;Stock Market Forecasting Techniques: A Survey&quot;, Volume: 06 Issue: 05 | May 2019, International Research Journal of Engineering and Technology (IRJET)</unstructured_citation></citation><citation key="ref7"><unstructured_citation>Divit Karmaini, Ruman Kazi, Ameya Nambisan, Aastha Shash, Vijaya Kamble, &quot;Comparison of Predictive Algorithms: Backpropagation, SVM, LSTM and Kalman Filter for Stock Market&quot;, 10.1109/AICAI.2019.8701258, IEEE</unstructured_citation></citation><citation key="ref8"><doi>10.1109/ICICI.2017.8365383</doi><unstructured_citation>Priyamvada, Rajesh Wadhvani, &quot;Review on various models for time series forecasting&quot;,Inventive Computing and Informatics (ICICI) International Conference on, pp. 405- 410, 2017.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>Aparna Nayak, M. M. Manohara Pai∗and Radhika M. Pai, &quot;Prediction Models for Indian Stock Market&quot;, Elsevier, ScienceDirect.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>Yoon, Y.; Swales, G. Predicting stock price performance: A neural network is an approach. In which Proceedings and of theTwenty-Fourth Annual Hawaii International Conference on System Sciences, Kauai, HI, USA, 8-11 January2019; pp. 156-162.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>Baba, N.; Kozaki, M. An intelligent forecasting system of stock price using neural networks. In Proceedings Of the 1992 IJCNN International Joint Conference on Neural Networks, Baltimore, MD, USA, 7-11 June 2018;pp. 371-377.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>Cheung, Y.-M.; Lai, H.Z.; Xu, L. Application of adaptive RPCL-CLP with trading system to foreign exchange the investment. In Proceedings for the International Conference on Neural Networks (ICNN'96), Washington,DC, USA, 3-6 June 2017; pp. 131-136.</unstructured_citation></citation><citation key="ref13"><doi>10.1109/72.728395</doi><unstructured_citation>Saad, E.W.; Prokhorov, D.V.; Wunsch, D.C. Comparative study of stock trend prediction using time delay,recurrent and probabilistic neural networks.IEEE Trans. Neural Netw.2017,9, 1456- 1470. [CrossRef]</unstructured_citation></citation><citation key="ref14"><unstructured_citation>Takahashi, T.; Tamada, R.; Nagasaka, K. Multiple line-segments regression for stock prices and long-range forecasting system by neural network. In Proceedings of the 37th SICE Annual Conference. InternationalSession Papers, Chiba, Japan, 29-31 July 2018; pp. 1127-1132.</unstructured_citation></citation><citation key="ref15"><doi>10.1016/S0925-2312(03)00372-2</doi><unstructured_citation>Kim, K.-J. Financial time series forecasting using support vector machines. Neurocomputing2017,55,307-319.</unstructured_citation></citation><citation key="ref16"><doi>10.1016/j.omega.2004.07.024</doi><unstructured_citation>Pai, P.-F.; Lin, C.-S. A hybrid ARIMA and support vector machines model in stock price forecasting.Omega 2018,33, 497-505. [CrossRef]</unstructured_citation></citation><citation key="ref17"><unstructured_citation>Manish, K.; Thenmozhi, M. Forecasting stock index movement: A comparison of support vector machines and the random forest. In Proceedings the of the Ninth Indian Institute of Capital Markets Conference, Mumbai,India, 19-20 December 2017.</unstructured_citation></citation><citation key="ref18"><doi>10.1016/j.cor.2004.03.016</doi><unstructured_citation>Huang, W.; Nakamori, Y.; Wang, S.-Y. Forecasting stock market movement direction with support vector machine.Computer. Oper. Res.2017,32, 2513-2522. [CrossRef]</unstructured_citation></citation><citation key="ref19"><unstructured_citation>Kumar, M.; Thenmozhi, M. Support vector machines approach to predict the S&amp;P CNX NIFTY index returns.SSRN Electron. J.2017. [CrossRef]</unstructured_citation></citation><citation key="ref20"><doi>10.1016/j.eswa.2006.08.020</doi><unstructured_citation>Chang, P.-C.; Liu, C.-H. A TSK type fuzzy rule based system for stock price prediction.Expert Syst. Appl.2018,34, 135-144. [CrossRef]</unstructured_citation></citation><citation key="ref21"><doi>10.1080/03081070601068595</doi><unstructured_citation>Ince, H.; Trafalis, T.B. Short term forecasting with support vector machines and application to stock price prediction.Int. J. Gen. Syst.2018,37, 677-687. [CrossRef]</unstructured_citation></citation><citation key="ref22"><doi>10.1016/j.eswa.2007.11.062</doi><unstructured_citation>Huang, C.-L.; Tsai, C.-Y. A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting.Expert Syst. Appl.2019,36, 1529-1539. [CrossRef]</unstructured_citation></citation><citation key="ref23"><doi>10.1016/j.eswa.2008.10.065</doi><unstructured_citation>Hsu, S.-H.; Hsieh, J.P.-A.; Chih, T.-C.; Hsu, K.-C. A two stage architecture is for a stock price forecasting by integrating self-organizing map and support vector regression.Expert Syst. Appl.2019,36, 7947-7951.[CrossRef]</unstructured_citation></citation><citation key="ref24"><doi>10.1016/j.eswa.2008.07.006</doi><unstructured_citation>Atsalakis, G.S.; Valavanis, K.P. Surveying stock market forecasting techniques-Part II: Soft computing methods.Expert Syst. Appl.2019,36, 5932-5941. [CrossRef]</unstructured_citation></citation></citation_list>
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