Login Register Cart 0
Generic placeholder image

Recent Patents on Engineering

Editor-in-Chief

ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Deterministic Weight Modification-based Extreme Learning Machine for Stock Price Prediction

Author(s): K. Kalaiselvi* and Vasantha Kalyani David

Volume 19, Issue 2, 2025

Published on: 04 December, 2023

Article ID: e041223224185 Pages: 12

DOI: 10.2174/0118722121268858231111180830

Price: $65

Abstract

Background: The prediction of the stock price is considered to be one of the most fascinating and important research and patent topics in the financial sector.

Aims: Making more accurate predictions is a difficult and significant task because the financial industry supports investors and the national economy.

Objectives: The DWM is used to adjust the connection weights and biases to enhance prediction precision and convergence rate. DWM was proposed as a method to reduce system error by changing the weights of various levels. The methods for predictable changes in weight were provided together with the computational difficulty.

Methods: An extreme learning machine (ELM) is a fast-learning method for training a singlehidden layer neural network (SLFN). However, the model's learning process is ineffective or incomplete due to the randomly chosen weights and biases of the input's hidden layers. Hence, this article presents a deterministic weight modification (DWM) based ELM called DWM-ELM for predicting the stock price.

Results: The calculated results showed that DWM-ELM had the best predictive performance, with RMSE (root mean square error) of 0.0096, MAE (mean absolute error) of 0.0563, 0.0428, MAPE (mean absolute percentage error) of 1.7045, and DS (Directional Symmetry) of 89.34.

Conclusion: The experimental results showed that, in comparison to other well-known prediction algorithms, the suggested DWM+ELM prediction model offers better prediction performance.

[1]
G.E. Box, G.M. Jenkins, G.C. Reinsel, and G.M. Ljung, Time series analysis: Forecasting and control., John Wiley & Sons, 2015.
[2]
J. Patel, S. Shah, P. Thakkar, and K. Kotecha, "Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques", Expert Syst. Appl., vol. 42, no. 1, pp. 259-268, 2015.
[http://dx.doi.org/10.1016/j.eswa.2014.07.040]
[3]
S. Asadi, E. Hadavandi, F. Mehmanpazir, and M.M. Nakhostin, "Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction", Knowl. Base. Syst., vol. 35, pp. 245-258, 2012.
[http://dx.doi.org/10.1016/j.knosys.2012.05.003]
[4]
K. Kalaiselvi, K. Velusamy, and C. Gomathi, "Financial prediction using back propagation neural networks with opposition based learning", J. Phys.: Conf. Ser., vol. 1142, p. 012008, 2018.
[http://dx.doi.org/10.1088/1742-6596/1142/1/012008]
[5]
V.K. David, "A survey on ontology based clinical practice guidelines with soft computing techniques", Int. J. Pure Appl. Math., vol. 118, no. 18, pp. 3047-3051, 2018.
[6]
C. Anand, "Comparison of stock price prediction models using pre-trained neural networks", J. Ubiquitous Computing Commun. Technol., vol. 3, no. 2, pp. 122-134, 2021.
[http://dx.doi.org/10.36548/jucct.2021.2.005]
[7]
H.K. Andi, "An accurate bitcoin price prediction using logistic regression with LSTM machine learning model", J. Soft Computing Paradigm, vol. 3, no. 3, pp. 205-217, 2021.
[http://dx.doi.org/10.36548/jscp.2021.3.006]
[8]
V.K. David, and S. Rajasekaran, Pattern recognition using neural and functional networks., Springer, 2009.
[http://dx.doi.org/10.1007/978-3-540-85130-1]
[9]
C.L. Lin, J.F. Wang, C.Y. Chen, C.W. Chen, and C.W. Yen, "Improving the generalization performance of RBF neural networks using a linear regression technique", Expert Syst. Appl., vol. 36, no. 10, pp. 12049-12053, 2009.
[http://dx.doi.org/10.1016/j.eswa.2009.03.012]
[10]
S. Rajasekaran, and R. Amalraj, "Predictions of design parameters in civil engineering problems using SLNN with a single hidden RBF neuron", Comput. Struc., vol. 80, no. 31, pp. 2495-2505, 2002.
[http://dx.doi.org/10.1016/S0045-7949(02)00213-4]
[11]
K. Velusamy, and R. Amalraj, Performance of the cascade correlation neural network for predicting the stock priceIn 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) Coimbatore, India, 2017, pp. 1-6.
[http://dx.doi.org/10.1109/ICECCT.2017.8117824]
[12]
K. Velusamy, and R. Amalraj, "Prediction of stock market using cascade correlation neural network with principal component analysis", Int. J. Eng. Technol., vol. 7, pp. 3485-3488, 2018.
[13]
X. Li, H. Xie, R. Wang, Y. Cai, J. Cao, F. Wang, H. Min, and X. Deng, "Empirical analysis: Stock market prediction via extreme learning machine", Neural Comput. Appl., vol. 27, no. 1, pp. 67-78, 2016.
[http://dx.doi.org/10.1007/s00521-014-1550-z]
[14]
M. Jiang, L. Jia, Z. Chen, and W. Chen, "The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm", Ann. Oper. Res., vol. 309, no. 2, pp. 553-585, 2022.
[http://dx.doi.org/10.1007/s10479-020-03690-w]
[15]
A.S.C. Alencar, A.R. Rocha Neto, and J.P.P. Gomes, "A new pruning method for extreme learning machines via genetic algorithms", Appl. Soft Comput., vol. 44, pp. 101-107, 2016.
[http://dx.doi.org/10.1016/j.asoc.2016.03.019]
[16]
F. Weng, Y. Chen, Z. Wang, M. Hou, J. Luo, and Z. Tian, "Gold price forecasting research based on an improved online extreme learning machine algorithm", J. Ambient Intell. Humaniz. Comput., vol. 11, no. 10, pp. 4101-4111, 2020.
[http://dx.doi.org/10.1007/s12652-020-01682-z]
[17]
L.L. Li, Z.F. Liu, M.L. Tseng, K. Jantarakolica, and M.K. Lim, "Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power", Expert Syst. Appl., vol. 184, p. 115579, 2021.
[http://dx.doi.org/10.1016/j.eswa.2021.115579]
[18]
S. Das, T.P. Sahu, R.R. Janghel, and B.K. Sahu, "Effective forecasting of stock market price by using extreme learning machine optimized by PSO-based group oriented crow search algorithm", Neural Comput. Appl., vol. 34, no. 1, pp. 555-591, 2022.
[http://dx.doi.org/10.1007/s00521-021-06403-x] [PMID: 34413575]
[19]
P. Yang, D. Wang, W.B. Zhao, L.H. Fu, J.L. Du, and H. Su, "Ensemble of kernel extreme learning machine based random forest classifiers for automatic heartbeat classification", Biomed. Signal Process. Control, vol. 63, p. 102138, 2021.
[http://dx.doi.org/10.1016/j.bspc.2020.102138]
[20]
Chandar, "Grey wolf optimization-elman neural network model for stock price prediction", Soft Comput., vol. 25, no. 1, pp. 649-658, 2021.
[http://dx.doi.org/10.1007/s00500-020-05174-2]
[21]
D. Selvamuthu, V. Kumar, and A. Mishra, "Indian stock market prediction using artificial neural networks on tick data", Financial Innovation, vol. 5, no. 1, p. 16, 2019.
[http://dx.doi.org/10.1186/s40854-019-0131-7]
[22]
D. Wu, X. Wang, and S. Wu, "A hybrid method based on extreme learning machine and wavelet transform denoising for stock prediction", Entropy, vol. 23, no. 4, p. 440, 2021.
[http://dx.doi.org/10.3390/e23040440] [PMID: 33918679]
[23]
Z. Tang, T. Zhang, J. Wu, X. Du, and K. Chen, "Multistep-ahead stock price forecasting based on secondary decomposition technique and extreme learning machine optimized by the differential evolution algorithm", Math. Probl. Eng., vol. 2020, 2020.
[24]
P. Khuwaja, S.A. Khowaja, I. Khoso, and I.A. Lashari, "Prediction of stock movement using phase space reconstruction and extreme learning machines", J. Exp. Theor. Artif. Intell., vol. 32, no. 1, pp. 59-79, 2020.
[http://dx.doi.org/10.1080/0952813X.2019.1620870]
[25]
Z. Cui, B. Huang, H. Dou, Y. Cheng, J. Guan, and T. Zhou, "A two-stage hybrid extreme learning model for short-term traffic flow forecasting", Mathematics, vol. 10, no. 12, p. 2087, 2022.
[http://dx.doi.org/10.3390/math10122087]
[26]
P. Chen, A. Vivian, and C. Ye, "Forecasting carbon futures price: A hybrid method incorporating fuzzy entropy and extreme learning machine", Ann. Oper. Res., vol. 313, no. 1, pp. 559-601, 2022.
[http://dx.doi.org/10.1007/s10479-021-04406-4] [PMID: 35002000]
[27]
G.B. Huang, Q.Y. Zhu, and C.K. Siew, "Extreme learning machine: Theory and applications", Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
[http://dx.doi.org/10.1016/j.neucom.2005.12.126]
[28]
S.C. Ng, C.C. Cheung, and S.H. Leung, "Magnified gradient function with deterministic weight modification in adaptive learning", IEEE Trans. Neural Netw., vol. 15, no. 6, pp. 1411-1423, 2004.
[http://dx.doi.org/10.1109/TNN.2004.836237] [PMID: 15565769]
[29]
C. Wang, and D.J. Hill, Deterministic learning theory for identification, recognition, and control., CRC Press, 2009.
[30]
K. Velusamy, and R. Amalraj, Cascade correlation neural network with deterministic weight modification for predicting stock market price.In IOP Conf. Ser.: Mater. Sci. Eng., vol. 1110, 2021, p. 012005
[http://dx.doi.org/10.1088/1757-899X/1110/1/012005]
[31]
K. Kalaiselvi, and V.K. David, Enhanced extreme learning machine algorithm with deterministic weight modification for investment decision on Indian stocks.In 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) Trichy, India, 2022, pp. 1409-1414.
[http://dx.doi.org/10.1109/ICOSEC54921.2022.9951899]
[32]
M.A.A. Albadr, S. Tiun, M. Ayob, and F.T. AL-Dhief, "Spoken language identification based on optimised genetic algorithm–extreme learning machine approach", Int. J. Speech Technol., vol. 22, no. 3, pp. 711-727, 2019.
[http://dx.doi.org/10.1007/s10772-019-09621-w]
[33]
J. Cao, Z. Lin, and G.B. Huang, "Self-adaptive evolutionary extreme learning machine", Neural Process. Lett., vol. 36, no. 3, pp. 285-305, 2012.
[http://dx.doi.org/10.1007/s11063-012-9236-y]
[34]
X. Wang, and M. Han, "Improved extreme learning machine for multivariate time series online sequential prediction", Eng. Appl. Artif. Intell., vol. 40, pp. 28-36, 2015.
[http://dx.doi.org/10.1016/j.engappai.2014.12.013]
[35]
F. Zhang, "Extreme learning machine for stock price prediction", Int. J. Electr. Eng. Educ., 2021.
[http://dx.doi.org/10.1177/0020720920984675]
[36]
J.Z. Wang, J.J. Wang, Z.G. Zhang, and S.P. Guo, "Forecasting stock indices with back propagation neural network", Expert Syst. Appl., vol. 38, no. 11, pp. 14346-14355, 2011.
[http://dx.doi.org/10.1016/j.eswa.2011.04.222]
[37]
"Yahoo finance", Available From: https://finance.yahoo.com/
[38]
Y. Kara, M. Acar Boyacioglu, and Ö.K. Baykan, "Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange", Expert Syst. Appl., vol. 38, no. 5, pp. 5311-5319, 2011.
[http://dx.doi.org/10.1016/j.eswa.2010.10.027]
[39]
Y. Zhang, and L. Wu, "Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network", Expert Syst. Appl., vol. 36, no. 5, pp. 8849-8854, 2009.
[http://dx.doi.org/10.1016/j.eswa.2008.11.028]

Rights & Permissions Print Cite
© 2025 Bentham Science Publishers | Privacy Policy