Realtime stock prediction using Transformations and Modeling
Keywords:
Stock, KPCA, ANFIS, Microsoft Azure, KPCA, RSI, EMA10, EMA20, ADO,Kubernetes, GRPC, Miro service, Clean Architecture, dockers, Load Balancer, Cloud SimulatorsAbstract
Real time stock prediction is growing demand with dynamic changing markets. Especially sentiments of the market are going to change the stock price dynamically, giving fund managers challenging scenario when they are investing. This paper proposes stock prediction model aiding stock agents to predict closing prices and current price of the stock. The algorithm models the stock tick and balance data parameters using KPCA Trans-formation and Artificial neural network model. Our stock predictor is cost optimized and end to end delay optimized to achieve real time stock prediction. Stock predictor is architected using Clean architecture and GRPC based microservices which gives the advantage of real time modifications, pluggability with minimum validation costs. Secure and cost-effective data backup features of the cloud will give additional cost advantage to the companies implementing the new stock predictor services.
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M. Th. Kotouza1, F. E. Psomopoulos and P. A. Mitkas1, “A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures”, Journal of cloud computing, 2020, 9:2.
N Aparna; M M; Manohara Pai; Radhika M Pai. Prediction Models of Indian Stock Market”, 12th international multi conference on information processing - 2016. Procedia Computer Science. 2016, 89, 441–449.
Kunal P, Neha A, “Stock Market Analysis using Supervise Machine Learning”, 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, India 14th-16th Feb 2019.
Xi Zhang, Si Qu, Jieyun H, Binxing F and Philip Y, “Stock Market Prediction via Multi-Source Multiple Instance Learning”, IEEE Access, Aug 27, Oct 8 2018, DOI 10.1109/ACCESS.2018.2869735.
K.Hiba Sadia, Adita S, Adaarrsh P, Sarmistha P, Saurav S, “Stock Market Prediction Using Machine Learning Algorithms”, IJEAT, ISSN: 2249-8958, Vol-8, Issue -4, April 2019.
Osman H, Omar S. S, and Mustafa A. S, “A Machine learning model for stock market prediction”, International Journal of Computer Science and Telecommunications, Vol-4, Issue -12, Dec 2013, pp 17-23.
Erkam G, Gulgan K and Tugrul U.D, “Using artificial neural netowrk models in stock market index prediction”, Expert systems with Applications, DOI 10.1016/j.eswa.2011.02.068.
Amin H. M, Moein H M, Morteza E, “Stock market index prediction using artificial neural network”, Journal of Economica, Fiance and Administrative Science, doi 10.1016/j.jefas.2016.07.002.
Weiwei Jiang, “Application of deep learning in stock market prediction: recent progress”, Elsevier Journal, Mar 5 2020.
Jonathan L. T, “A Bayesian regularized artificial neural network for stock market forecasting”, Elsevier,Expert systems with Applications, doi 10.1016/j.eswa.2013.04.013
Dharmaraj S, Veneet K and Abhishek M, “ Indian stock market prediction using artificial neural networks on tick data” Financial Evolution, doi 10.1186/s40854-019-0131-7.
Nirbhay N, “Stock Market Prediction using Machine Learning and Cloud Computing”, International Journal of Engineering And Computer Science, Vol-8 Issue 9 Sept 2019, pp No. 24847-24850.
Jyh-Shing Roger Jang, “ANFIS:Adaptive-Network-Based Fuzzy Inference System”, IEEE Transactions on Systems Man and Cybernetics, June 1993.
Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Ben Ahmed, "A Neural Networks Based Approach for the Real-Time Scheduling of Reconfigurable Embedded Systems with Minimization of Power Consumption.", ICIS Conference, At Okayama, Japan, June 2016, 10.1109/ICIS.2016.7550777.
S. Agantonovic-kustrin, R. Beresford, “Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research”, Journal of Pharmaceutical and Biomedical Analysis”, 22(2000) 717-727.
M.V.S Phani Narasimham, Dr. Y.V.S Sai Pragathi, “Development of realistic models of oil well by modeling porosity using modified ANFIS technique”, International Journal on Computer Science and Engineering, Vol.11, No.07, July 2019.
Alberto Nunez, Jose L.Vázquez-Poletti, Agustin C. Caminero, Gabriel G. Castañé, Jesus Carretero, Ignacio
M. Llorente, “iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator”, J Grid Computing (2012) 10:185–209.
Roland H. Steinegger, P. Giessler, B. Hippchen and Sebastian Abeck, “Overview of a Domain-Driven Design Approach to Build Microservice-Based Applications “, SOFTENG 2017: The third International conference on Advances and Trends in Software , ISBN: 978-1-61208-553-1.
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