Univariate Financial Time Series Prediction using Clonal Selection Algorithm

Ammar Azlan, Yuhanis Yusof, Mohamad Farhan Mohamad Mohsin

Abstract


The ability to predict the financial market is beneficial not only to the individual but also to the organization and country. It is not only beneficial in terms of financial but also in terms of making a short-term and long-term decision. This paper presents an experimental study to perform univariate financial time series prediction using a clonal selection algorithm (CSA). CSA is an optimization algorithm that is based on clonal selection theory. It is a subset of the artificial immune system, a class of evolutionary algorithms inspired by the immune system of a vertebrate. Since CSA is an optimization algorithm, the univariate financial time series prediction problem was modeled into an optimization problem using a weighted regression model. CSA was used to search for the optimal set of weights for the regression model to generate prediction with the lowest error. Three data sets from the financial market were chosen for the experiments of this study namely S&P500 price, Gold price, and EUR-USD exchange rate. The performance of CSA is measured using RMSE. The value of RMSE for a problem is related to the maximum and minimum value of the data set. Therefore, the results were not compared to other data sets. Instead, it is compared to the range of values of the data sets. The result of the experiments shows that CSA can make decent predictions for financial time series despite being inferior to ARIMA. Hence, this finding implies that CSA can be implemented on a univariate financial time series prediction problem given that the problem is modeled as an optimization problem.


Keywords


artificial immune system; clonal selection algorithm; financial time series; prediction; univariate.

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References


R. B. Singh, “Financial Markets,” in The DBS Handbook of Finance, 1st ed., DBS Imprints, 2014, p. 159.

W. Kenton, “New York Stock Exchange - NYSE,” 2018. .

O. Kwon, S. Rahmatian, A. Iriberri, and Z. Wu, “Comparison of Neural Network and Ordinary Least Squares Models in Forecasting Chinese Stock Prices,” Int. J. Bus. Econ., vol. 1, no. 1, pp. 1–17, Mar. 2018.

X.-Y. Qian, “Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods,” pp. 1–9, 2017.

G. Mahalakshmi, S. Sridevi, and S. Rajaram, “A survey on forecasting of time series data,” in 2016 International Conference on Computing Technologies and Intelligent Data Engineering, ICCTIDE 2016, 2016, pp. 1–8.

L. A. Laboissiere, R. A. S. Fernandes, and G. G. Lage, “Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks,” Appl. Soft Comput., vol. 35, pp. 66–74, 2015.

S. Bouktif et al., “Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches,” Energies, vol. 11, no. 7, p. 1636, Jun. 2018.

Y. Al-Douri, H. Hamodi, and J. Lundberg, “Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans,” Algorithms, vol. 11, no. 8, p. 123, Aug. 2018.

V. Manahov and H. Zhang, “Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming,” Int. J. Electron. Commer., vol. 23, no. 1, pp. 12–32, Jan. 2019.

J. Timmis, A. Hone, T. Stibor, and E. Clark, “Theoretical advances in artificial immune systems,” Theor. Comput. Sci., vol. 403, no. 1, pp. 11–32, Aug. 2008.

D. Dasgupta, S. Yu, and F. Nino, “Recent advances in artificial immune systems: Models and applications,” Appl. Soft Comput. J., vol. 11, no. 2, pp. 1574–1587, Mar. 2011.

R. Syahputra and I. Soesanti, “An artificial immune system algorithm approach for reconfiguring distribution network,” in AIP Conference Proceedings, 2017, vol. 1867, p. 20019.

G. Cheng, “Unattended remote attestation delegation for grid computing,” 2009.

H. S. Bernardino and H. J. C. Barbosa, “Artificial Immune Systems for Optimization,” Springer, Berlin, Heidelberg, 2009, pp. 389–411.

C. Liang and L. Peng, “An Automated Diagnosis System of Liver Disease using Artificial Immune and Genetic Algorithms,” J. Med. Syst., vol. 37, no. 2, p. 9932, Apr. 2013.

J. Timmis, “Artificial immune systems—today and tomorrow,” Nat. Comput., vol. 6, no. 1, pp. 1–18, Feb. 2007.

Y. Peng and B.-L. Lu, “Hybrid learning clonal selection algorithm,” Inf. Sci. (Ny)., vol. 296, pp. 128–146, Mar. 2015.

W. Pang, K. Wang, Y. Wang, G. Ou, H. Li, and L. Huang, “Clonal Selection Algorithm for Solving Permutation Optimisation Problems: A Case Study of Travelling Salesman Problem,” in Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science, 2015.

N. Xu, Y. Ding, L. Ren, and K. Hao, “Degeneration Recognizing Clonal Selection Algorithm for Multimodal Optimization,” IEEE Trans. Cybern., vol. 48, no. 3, pp. 848–861, Mar. 2018.

W. Luo and X. Lin, “Recent advances in clonal selection algorithms and applications,” in 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017, pp. 1–8.

Y.-C. Chou, Y.-H. Fan, M. Nakajima, and Y.-L. Liao, “Constrained design optimization of active magnetic bearings through an artificial immune system,” Eng. Comput., vol. 33, no. 8, pp. 2395–2420, Nov. 2016.

G. Pan, K. Li, A. Ouyang, and K. Li, “Hybrid immune algorithm based on greedy algorithm and delete-cross operator for solving TSP,” Soft Comput., vol. 20, no. 2, pp. 555–566, Feb. 2016.

Z. Li, X. Yan, Y. Fan, and K. Tang, “Improved Clonal Selection Algorithm for Solving AVO Elastic Parameter Inversion Problem,” in Qiao J. et al. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, Springer, Singapore, 2018, pp. 60–69.

Y. Hu, X. Sun, X. Nie, Y. Li, and L. Liu, “An Enhanced LSTM for Trend Following of Time Series,” IEEE Access, pp. 1–1, 2019.

L. N. de Castro and F. J. Von Zuben, “Learning and optimization using the clonal selection principle,” IEEE Trans. Evol. Comput., vol. 6, no. 3, pp. 239–251, Jun. 2002.

E. Hadavandi, A. Ghanbari, and S. Abbasian-Naghneh, “Developing a Time Series Model Based on Particle Swarm Optimization for Gold Price Forecasting,” in 2010 Third International Conference on Business Intelligence and Financial Engineering, 2010, pp. 337–340.




DOI: http://dx.doi.org/10.18517/ijaseit.10.1.10235

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