Abstract
Big data evolves as a new research domain in the era of 21st century. This domain concerns with the study of voluminous data sets with multiple factors, whose sizes are rapidly growing with the time. These types of data sets can be generated from various autonomous sources, such as scientific experiments, engineering applications, government records, financial activities, etc. With the rise of big data concept, demand for a new time series prediction models emerged. For this purpose, a novel big data time series forecasting model is introduced in this chapter, which is based on the hybridization of two soft computing (SC) techniques, viz., fuzzy set and artificial neural network. The proposed model is explained with the stock index price data set of State Bank of India (SBI). The performance of the model is verified with different factors, viz., two-factors, three-factors, and M-factors. Various statistical analyzes signify that the proposed model can take far better decision with the M-factors data set.
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Singh, P. (2015). Big Data Time Series Forecasting Model: A Fuzzy-Neuro Hybridize Approach. In: Acharjya, D., Dehuri, S., Sanyal, S. (eds) Computational Intelligence for Big Data Analysis. Adaptation, Learning, and Optimization, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-16598-1_2
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DOI: https://doi.org/10.1007/978-3-319-16598-1_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16597-4
Online ISBN: 978-3-319-16598-1
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