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International Journal of Sensors, Wireless Communications and Control

Author(s): Cerene Mariam Abraham*, Mannathazhathu Sudheep Elayidom and Thankappan Santhanakrishnan

DOI: 10.2174/2210327910666200304141238

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Big Data Analysis for Trend Recognition Using Machine Learning Techniques

Page: [540 - 550] Pages: 11

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Abstract

Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets.

Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data.

Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work include model generation using ARIMA, Hadoop framework which comprises mapping and reducing for big data analysis.

Results: The results of this work are the observation of a trend that indicates the rise and fall of price in derivatives , generation of time-series similarity graph and plotting of frequency of temporal data.

Conclusion: Big data analytics is an underexplored topic in the Indian derivative market and the results from this paper can be used by investors to earn both short-term and long-term benefits.

Keywords: Big data, derivative market, open interest, deliverable quantity, ARIMA, temporal data mining, machine learning.

Graphical Abstract

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