Version 1
: Received: 29 October 2023 / Approved: 30 October 2023 / Online: 31 October 2023 (02:59:40 CET)
How to cite:
Deshmukh, S.J.; Talekar, S. The Symphony of Algorithms:Harmonizing Hybrids for Stock Market Prognostication. Preprints2023, 2023101957. https://doi.org/10.20944/preprints202310.1957.v1
Deshmukh, S.J.; Talekar, S. The Symphony of Algorithms:Harmonizing Hybrids for Stock Market Prognostication. Preprints 2023, 2023101957. https://doi.org/10.20944/preprints202310.1957.v1
Deshmukh, S.J.; Talekar, S. The Symphony of Algorithms:Harmonizing Hybrids for Stock Market Prognostication. Preprints2023, 2023101957. https://doi.org/10.20944/preprints202310.1957.v1
APA Style
Deshmukh, S.J., & Talekar, S. (2023). The Symphony of Algorithms:Harmonizing Hybrids for Stock Market Prognostication. Preprints. https://doi.org/10.20944/preprints202310.1957.v1
Chicago/Turabian Style
Deshmukh, S.J. and Sopan Talekar. 2023 "The Symphony of Algorithms:Harmonizing Hybrids for Stock Market Prognostication" Preprints. https://doi.org/10.20944/preprints202310.1957.v1
Abstract
Stock market prediction is a challenging task to perform, as we know the fluctuations that take place in the market makes it versatile and hard to predict
the prices. In this research, we have explored the power of usage of hybrid
ensemble algorithms to improve the predictive accuracy in the stock market forecasting. Our research comprises construction and evaluation of diverse hybrid
models using different algorithms. The methodology presented in the paper
involves comprehensive data preparation, feature engineering, and model normalization. Evaluating the different hybrid models, one stands out distinctly: LSTM
(long short-term memory networks) + GRU (Gated recurrent units) + Conv1D
(one-dimensional convolutional layer) hybrid. It illustrated its potential to revolutionize decision-making tools for investors and financial analysts in stock market
analytics. The harmonious integration of algorithms not only underscores the
effectiveness of hybrid modeling but also beckons for further exploration within
the ever-evolving domain of predictive modeling, driven by the pursuit of precision and accuracy. This model shows a remarkable accuracy metrics including a
Mean Absolute Error (MAE) of 0.95, a Mean Squared Error (MSE) of 2.1222, a
Root Mean Squared Error (RMSE) of 1.52, and a R-squared (R2) score of 0.9982.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
