Optimizing Hyperparameters for Enhanced LSTM-Based Prediction System Performance

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10s.7620
Keywords:
deep learning, hyperparameter calibration, LSTM, stock price prediction, yahoo finance

Main Article Content

Priyanka Paygude
Associate Professor, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India
Prashant Chavan
Assistant Professor, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India
Milind Gayakwad
Assistant Professor, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India
Khushi Gupta
Student Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India
Satyam Joshi
Student Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India
Gopika
Student Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India
Rahul Joshi
Associate Professor, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
Sudhanshu Gonge
Assistant Professor, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
Ketan Kotecha
Professor Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India

Abstract

This research paper explores the application of deep learning and supervised machine learning algorithms, specifically Long Short-Term Memory (LSTM), for stock market prediction. The study focuses on the closing prices of three companies - Tata Steel, Apple, and Powergrid - using a dataset sourced from Yahoo Finance. Performance evaluation of the LSTM model employed RMSE, MAPE, and accuracy metrics, along with hyperparameter calibration to determine the optimal model parameters. The findings indicate that a single-layer LSTM model outperformed a multilayer LSTM model across all companies and evaluation metrics. Furthermore, a comparison with existing research demonstrated the superiority of the proposed model. The study emphasizes the effectiveness of LSTM models for stock price prediction, underscores the significance of proper hyperparameter tuning for optimal performance, and concludes that a single-layer LSTM model can yield superior results compared to a multilayer model.

Article Details

How to Cite
Paygude, P. ., Chavan, P. ., Gayakwad, M. ., Gupta, K. ., Joshi, S. ., Gopika, G., Joshi, R., Gonge, S., & Kotecha, K. . (2023). Optimizing Hyperparameters for Enhanced LSTM-Based Prediction System Performance . International Journal on Recent and Innovation Trends in Computing and Communication, 11(10s), 203–213. https://doi.org/10.17762/ijritcc.v11i10s.7620
Issue
Vol. 11 No. 10s (2023)
Section
Articles

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