Abstract
Forecasting is a technique commonly used in the study of time series to forecast a variable response for a specified period of time, such as monthly earnings, stock performance, or unemployment figures. Forecasting is historical data behavior to determine the direction of future trends. Therefore, many machine learning algorithms are used in the past few years. In this study, a summary of an extreme learning machine with MapReduce technique (ELM_MapReduce) is presented. This technique is based on the concept of processing large amount of historical data and application of extreme learning machine to achieve fast learning speed. As stock market data is large set of historical data that need time to process, MapReduce method is used to handle such limitations. The technique shows the advantages and disadvantages of using MapReduce method in ELM and can be used in different areas of research.
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References
Khoa, N.L.D., Sakakibara, K., Nishikawa, I.: Stock price forecasting using back propagation neural networks with time and profit based adjusted weight factors. In: 2006 SICE-ICASE International Joint Conference, pp. 5484–5488. IEEE (2006)
Wang, J.H., Leu, J.Y.: Stock market trend prediction using ARIMA-based neural networks. In: Proceedings of International Conference on Neural Networks (ICNN’96), vol. 4, pp. 2160–2165. IEEE (1996)
Cao, Q., Leggio, K. B., Schniederjans, M.J.: A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Comput. Oper. Res. 32(10), 2499–2512 (2005)
Moghaddam, A.H., Moghaddam, M. H., Esfandyari, M.: Stock market index prediction using artificial neural network. J. Econ. Finan. Admin. Sci. 21, 89–93 (2016)
Sivalingam, K.C., Mahendran, S., Sivanandam Natarajan, S.: Forecasting gold prices based on extreme learning machine. Int. J. Computers. Commun. Control 11: 372–380 (2016)
Li, X., Xie, H., Wang, R., Cai, Y., Cao, J., Wang, F., Deng, X., et al.: Empirical analysis: stock market prediction via extreme learning machine. Neural Comput. Appl. 27(1), 67–78 (2016)
Huang, G., Huang, G.B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)
Huang, G.-B., Chen, L.: Convex incremental extreme learning machine. Neurocomputing 70(16–18): 3056–3062 (2007)
Huang, G-B.: An insight into extreme learning machines: random neurons, random features and kernel. Cogn. Comput. 6:376–390 (2014)
Huang, G.-B.: What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle. Cogn. Comput. 7, 263–278 (2015)
Rakha, M.A.: On the Moore-Penrose generalized inverse matrix. Appl. Math. Comput. 158, 185–200 (2004)
Jeffrey, D., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)
Chen, J., Chen, H., Wan, X., Zheng, G.: MR-ELM: a MapReduce-based framework for large-scale ELM training in big data era. Neural Comput. Appl. 27(1), 101–110 (2016)
Liu, Y., Xu, L., Li, M.: The parallelization of back propagation neural network in mapreduce and spark. Int. J. Parallel Prog. 45(4), 760–779 (2017)
Venkatraman, S., Kulkarni, S.: MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud. In. 2012 12th International Conference on Hybrid Intelligent Systems (HIS), pp. 63–68, IEEE (2012)
Namitha, K., Jayapriya, A., Kumar, G. S.: Rainfall prediction using artificial neural network on map-reduce framework. In. Proceedings of the Third International Symposium on Women in Computing and Informatics, pp. 492–495, ACM (2015)
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Rath, S., Tripathy, A., Swagatika, S. (2021). Application of ELM-MapReduce Technique in Stock Market Forecasting. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 153. Springer, Singapore. https://doi.org/10.1007/978-981-15-6202-0_48
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DOI: https://doi.org/10.1007/978-981-15-6202-0_48
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