Deciphering Market Dynamics: A Data Science and Machine Learning Approach Using Chaos Theory for Trend Prediction

Authors

  • K. Prabhakar Professor, Dept. of AIML, Chaitanya Bharathi Institute of Technology-Gandipet, Hyderabad, India,
  • Manjula V. Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai,
  • P. Punitha Associate professor Department of CSE( Data Science) Vignana Bharathi Institute of Technology.
  • Khasim Vali Dudekula Assistant Professor, School of Computer Science and Engineering, VIT-AP UNIVERSITY
  • Panduranga Ravi Teja Assistant Professor, School of Computing, University of Petroleum & Energy Studies, Dehradun

Keywords:

Chaos Theory, Time Delay Embedding, Attractor Reconstruction, Trend Prediction, Financial markets

Abstract

This study introduces an innovative technique for the prediction of financial market movements by leveraging chaos theory principles. Employing time-delay embedding alongside attractor reconstruction, the research discerns critical structures within financial market time series data. The identification of these patterns facilitates the creation of a predictive model aimed at forecasting forthcoming market behaviours. The findings of the research acknowledge the persistent challenge posed by the unpredictable nature of financial markets; however, the application of a chaos theory framework offers valuable perspective into the intricate mechanisms governing these sophisticated systems. This paper's approach highlights the potential of chaos theory as a tool in deciphering and anticipating the fluctuations of financial markets, thereby contributing to the fields of economic forecasting and financial analyse

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Published

13.12.2023

How to Cite

Prabhakar, K. ., V., M. ., Punitha, P. ., Dudekula, K. V. ., & Ravi Teja, P. . (2023). Deciphering Market Dynamics: A Data Science and Machine Learning Approach Using Chaos Theory for Trend Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 426–434. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4142

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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