Predicting Daily Stock Market Price using a Few-shot and Modified Transfer Learning

Authors

  • M. Dhivya Research Scholar, Department of Computer Science, A.V.V.M Sri Pushpam College (Autonomous) Poondi, Thanjavur. Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.
  • V. Maniraj Associate Professor & Research Supervisor, Department of Computer Science, A.V.V.M Sri Pushpam College (Autonomous) Poondi, Thanjavur. Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.

Keywords:

methods, performance, requirement, APR-FSL

Abstract

Predicting the stock market and future stock prices is a difficult task. Stock customers have a strong requirement for market estimation. However, it frequently fails to produce successful results when predicting the stock price using a small amount of previous data. The Adaptive Deep ResNet with Few-Shot Learning deep model, which is new and built on transfer learning and Few-shot learning, is used in this research (APR-FSL). This work aims to maximize stock market price prediction performance, which presently offers predictions with the highest accuracy and lowest error rates. The proposed methodology focused on enhancing stock market prediction by fusing a Few-shot learning and knowledge transfer from transfer learning. Experiment results on the huge stock market dataset showed that the APR-FSL model outperforms other existing methods in terms of accuracy.

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Published

24.11.2023

How to Cite

Dhivya, M. ., & Maniraj, V. . (2023). Predicting Daily Stock Market Price using a Few-shot and Modified Transfer Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(5s), 400–408. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3921

Issue

Vol. 12 No. 5s (2024)

Section

Research Article

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