Comparative Analysis of Companies Stock Price Prediction Using Time Series Algorithm
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Lakshmana Phaneendra Maguluri, R. Ragupathy
|DOI : 10.14445/22315381/IJETT-V68I11P202|
MLA Style: Lakshmana Phaneendra Maguluri, R. Ragupathy "Comparative Analysis of Companies Stock Price Prediction Using Time Series Algorithm" International Journal of Engineering Trends and Technology 68.11(2020):9-15.
APA Style:Lakshmana Phaneendra Maguluri, R. Ragupathy. Comparative Analysis of Companies Stock Price Prediction Using Time Series Algorithm International Journal of Engineering Trends and Technology, 68(11),9-15.
As the Company’s financial values change day-by-day with uncertainty, forecasting of the stock market prices is a challenging task. The motive to forecast the stock market prices is to ensure that the investor’s gains profit in what they are investing in. One of the core areas in stock market prediction is time series forecasting by using machine learning algorithms. This paper uses one of the time series prediction models called Auto-Regressive Integrated Moving Average (ARIMA) for future price prediction and forecasting.
 B. Krollner, B. Vanstone, and G. Finnie, Financial Time Series Forecasting with Machine Learning Techniques: A Survey, in Proc. ESANN 2010, pp.1-7, 28-30 April 2010.
 G. Bontempi, S. B. Taieb, and Y. Borgne, Machine Learning Strategies for Time Series Forecasting, in Proc. eBISS 2012: Business Intelligence, pp.62-77, 2013.
 N. K. Ahmed, A. F. Atiya, N. El. Gayar, and H. El-Shishiny, An Empirical Comparison of Machine Learning Models for Time Series Forecasting, Journal Econometric Reviews, 29(5-6) (2010) 594-621.
 S.Panigrahi, and H.S. Behera, A study on leading machine learning techniques for high order fuzzy time series forecasting Engineering Applications of Artificial Intelligence Elsevier, 87 (2010) 1-10 .
 S.Kaushik, A.Choudhury, N.Dasgupta, S.Natarajan, L. A. Pickett, and V. Dutt, Ensemble of multi-headed machine learning architectures for time-series forecasting of healthcare expenditures, Algorithms for Intelligent Systems, Applications of Machine Learning. chapter.14 (2020) 199-216.
 J.A.Fischer, P.Pohl, and D. Ratz, A machine learning approach to univariate time series forecasting of quarterly earnings”, Review of Quantitative Finance and Accounting. (2020)1163-1179.
 D. S. De, J.F.L. de Oliveira, and P. S.G. de M. Neto, An intelligent hybridization of ARIMA with machine learning models for time series forecasting, Knowledge-Based Systems. 175 (2019) 72-86.
 B. M. Pavlyshenko, Machine-Learning Models for Sales Time Series Forecasting, In Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 4(10) (2018) 1-11.
 V. Cerqueira, L. Torgo, and C. Soares, Machine learning vs statistical methods for time series forecasting: Size matters, ,arXiv preprint arXiv:1909.13316, 2019
 C. Fan, Y. Zhang,Y.Pan, X. Li, C.Zhang, R. Yuan, D.Wu, W.Wang, J. Pei, and H.Huang, Multi-Horizon Time Series Forecasting with Temporal Attention Learning, KDD `19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (2019) 2527–2535.
 H. Shih, and S. Rajendran, Comparison of time series methods and machine learning algorithms for forecasting Taiwan Blood Services Foundation`s blood supply, Journal of healthcare engineering, 2019 (2019) 1-6.
 G. Papacharalampous, H.Tyralis, and D. Koutsoyiannis, Univariate time series forecasting of temperature and precipitation with a focus on machine learning algorithms: A multiple-case study from Greece, Water Resources Management, 32 (2018) 5207–5239.
 C. Deb, F. Zhang, J. Yang, SE. Lee, and KW. Shah, A review on time series forecasting techniques for building energy consumption, Renewable and Sustainable Energy Reviews, 74 (2017) 902-924.
 L. P. Maguluri, and R. Ragupathy, A New Sentiment Score Based Improved Bayesian Networks For Real-Time Intraday Stock Trend Classification, International Journal of Advanced Trends in Computer Science and Engineering. 8(4) (2019) 1045-1055.
 L. P. Maguluri, and R. Ragupathy, An Efficient Stock Market Trend Prediction Using the Real-Time Stock Technical Data and Stock Social Media Data, International Journal of Intelligent Engineering and Systems. 13(4) (2020) 316-332.
 L. P. Maguluri, and R. Ragupathy, A Cluster Based Non-Linear Regression Framework for Periodic Multi-Stock Trend Prediction on Real Time Stock Market Data, International Journal of Advanced Computer Science and Applications. 11(9) (2020) 537-551.
 M. Syamala, and N. J. Nalini, A deep analysis on aspect based sentiment text classification approaches, International Journal of Advanced Trends in Computer Science and Engineering. 85 (2019) 1795-1801.
 M. Syamala, and N.J. Nalini, A filter based improved decision tree sentiment classification model for real-time amazon product review data, International Journal of Intelligent Engineering and Systems. 13(1) (2020) 191-201.
Time Series Forecasting, Formatting, ARIMA, Stock Price Prediction, Trend.