Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index
Portfolio theory underpins portfolio management, a much-researched yet uncharted field. Stock market prediction is a challenging and essential endeavour in financial research, owing to the nonlinear, volatile, and stochastic characteristics of financial time series data. Conventional statistical tec...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11005722/ |
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| author | Priya Singh Manoj Jha Harshita Patel |
| author_facet | Priya Singh Manoj Jha Harshita Patel |
| author_sort | Priya Singh |
| collection | DOAJ |
| description | Portfolio theory underpins portfolio management, a much-researched yet uncharted field. Stock market prediction is a challenging and essential endeavour in financial research, owing to the nonlinear, volatile, and stochastic characteristics of financial time series data. Conventional statistical techniques often fall short to encapsulate complex interdependencies, resulting in diminished predictive accuracy. This research proposes an ensemble model that integrates Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Temporal Convolutional Networks (TCN) for effective stock market prediction. The Nifty 50 index dataset is utilized for the empirical evidences. Wavelet-based denoising is utilised as a preprocessing measure to mitigate the intrinsic noise in stock market data. The model’s efficacy is assessed utilising error metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>). The five-fold cross-validation is utilized to establish the robustness of the models. Furthermore, we ascertain the statistical significance of performance enhancements by parametric t-tests, including normality assessments via the Shapiro-Wilk test. Moreover, current state-of-the-art models advocates in favour of proposed study. |
| format | Article |
| id | doaj-art-3f5d89a26c6f4fcca7502fadb1c812d9 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3f5d89a26c6f4fcca7502fadb1c812d92025-08-20T03:13:42ZengIEEEIEEE Access2169-35362025-01-0113870368704710.1109/ACCESS.2025.356863411005722Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 IndexPriya Singh0https://orcid.org/0000-0002-0625-9617Manoj Jha1Harshita Patel2https://orcid.org/0000-0002-9381-9028School of Advanced Sciences, Vellore Institute of Technology, Vellore, IndiaDepartment of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, IndiaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, IndiaPortfolio theory underpins portfolio management, a much-researched yet uncharted field. Stock market prediction is a challenging and essential endeavour in financial research, owing to the nonlinear, volatile, and stochastic characteristics of financial time series data. Conventional statistical techniques often fall short to encapsulate complex interdependencies, resulting in diminished predictive accuracy. This research proposes an ensemble model that integrates Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Temporal Convolutional Networks (TCN) for effective stock market prediction. The Nifty 50 index dataset is utilized for the empirical evidences. Wavelet-based denoising is utilised as a preprocessing measure to mitigate the intrinsic noise in stock market data. The model’s efficacy is assessed utilising error metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>). The five-fold cross-validation is utilized to establish the robustness of the models. Furthermore, we ascertain the statistical significance of performance enhancements by parametric t-tests, including normality assessments via the Shapiro-Wilk test. Moreover, current state-of-the-art models advocates in favour of proposed study.https://ieeexplore.ieee.org/document/11005722/Ensemble learningstock marketrandom forestLSTMTCNCNN |
| spellingShingle | Priya Singh Manoj Jha Harshita Patel Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index IEEE Access Ensemble learning stock market random forest LSTM TCN CNN |
| title | Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index |
| title_full | Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index |
| title_fullStr | Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index |
| title_full_unstemmed | Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index |
| title_short | Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index |
| title_sort | wavelet enhanced deep learning ensemble for accurate stock market forecasting a case study of nifty 50 index |
| topic | Ensemble learning stock market random forest LSTM TCN CNN |
| url | https://ieeexplore.ieee.org/document/11005722/ |
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