پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 676 |
| تعداد مقالات | 9,850 |
| تعداد مشاهده مقاله | 69,922,673 |
| تعداد دریافت فایل اصل مقاله | 49,232,291 |
Financial timeseries prediction by a hybrid model of chaos theory, multi-layer perceptron and metaheuristic algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 17، دوره 16، شماره 2، اردیبهشت 2025، صفحه 209-217 اصل مقاله (795.02 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.31367.4621 | ||
| نویسندگان | ||
| Mostafa Sohouli Vahed1؛ Mohammad Ali Aghaei* 2؛ Fariborz Avazzadeh Fath3؛ Ali Pirzad4 | ||
| 1Department of Accounting, Yasuj Branch, Islamic Azad University, Yasuj, Iran | ||
| 2Department of Accounting, Tarbiat Modares University, Tehran, Iran | ||
| 3Department of Accounting, Gachsaran Branch, Islamic Azad University, Gachsaran, Iran | ||
| 4Department of Management, Yasuj Branch, Islamic Azad University, Yasuj, Iran | ||
| تاریخ دریافت: 17 اردیبهشت 1402، تاریخ پذیرش: 04 مرداد 1402 | ||
| چکیده | ||
| Many researchers proved that hybrid models have better results in comparison with independent models. A combination of different methods could enhance the accuracy of time series prediction. Hence, this research used the hybrid of three methods of chaos theory, multi-layer perceptron and metaheuristic algorithm to increase the power of the model forecasting. Artificial neural networks have properly considered complex nonlinear relations and are good comprehensive approximators. Multi-objective evolutionary algorithms such as multi-objective particle swarm optimization are good at solving multi-objective optimization issues. This algorithm organized the combination of parent and children populations by elitist strategy, decreased the messy comparing factors to improve the solution variety and avoided to use of niche factors. Chaos theory controls the complexities of stochastic systems. So, this research offers Tehran Stock Exchange Index (TSEI) prediction by a hybrid model of chaos theory, multi-layer perceptron and metaheuristic algorithm. The results show that in perceptron-based mode, RMSE measures are gradually increased in all intervals. The continuous decrease of RMSE shows that the perceptron-based model could show consistency with the whole data flow. This matter could offer a better learning and consistency process by perceptron-based models to predict stock prices, as this type of learning could apply more experiences for forecasting future behaviour in order to change the system content. | ||
| کلیدواژهها | ||
| Financial timeseries؛ Chaos theory؛ Multi-layer perceptron؛ Metaheuristic algorithm | ||
| مراجع | ||
|
[1] H. Akaike, A new look at the statistical model identification, IEEE Trans. Automatic Control 19 (1974), no. 6, 716–723. [2] J.M. Bates and C.W.J. Granger, The combination of forecasts, Oper. Res. Q. 20 (1969). [3] G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, 1976. [4] L. Cao, Practical method for determining the minimum embedding dimension of a scalar time series, Phys. D: Nonlinear Phen. 110 (1997), no. 1-2, 43–50. [5] C. Chatfield, Model uncertainty and forecast accuracy, J. Forecast. 15 (1996), 495–508. [6] R.T. Clemen, Combining forecasts: A review and annotated bibliography, Int. J. Forecast. 5 (1989), 559–583. [7] A. Coello Coello, G.B. Lamont, and D.A. Van Veldhuisen, Evolutionary Algorithms for Solving Multi-Objective Problems, (2nd ed.), Springer series of Genetic Algorithms and Evolutionary Computation, 2007. [8] K. Deb, A. Pratap, S. Agarwal and T.A.M.T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2002), no. 2, 182–197. [9] I. Ginzburg and D. Horn, Combined neural networks for time series analysis, Adv. Neural Inf. Process. Syst. 6 (1993). [10] S. Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, and R. Winkler, The accuracy of extrapolation (time series) methods: Results of a forecasting competition, J. Forecast. 1 (1982), 111–153. [11] S. Makridakis, S. Wheelwright, and R. Hyndman, Forecasting: Methods and Applications, edition 3 ed., New York: John Wiley & Sons, 1998. [12] T. Mitsa, Temporal Data Mining, Chapman & Hall/ CRC Data mining and knowledge discovery series, CRC Press, US, 2010. [13] A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, and C.A. Coello Coello, A survey of multiobjective evolutionary algorithms for data mining: Part I, IEEE Trans. Evolut. Comput. 18 (2014), 4–19. [14] N.H. Packard, J.P. Crutchfield, J.D. Farmer, and R.S. Shaw, Geometry from a time series, Phys. Rev. Lett. 45 (1980), no. 9, 712. [15] H. Poincare, Sur le probl`eme des trois corps et les equations de la dynamique, Acta Math. 13 (1890), no. 1, A3–A270. [16] D.J. Reid, Combining three estimates of gross domestic product, Economica 35 (1968). [17] N. Srinivas and K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evolut. Comput. 2 (1994), 221–248. [18] J.L. Ticknor, A Bayesian regularized artificial neural network for stock market forecasting, Expert Syst. Appl. 40 (2013), no. 14, 5501–5506. [19] G. Zhang, B. Patuwo, and M.Y. Hu, Forecasting with artificial neural networks:: The state of the art, Int. J. Forecast. 14 (1998). | ||
|
آمار تعداد مشاهده مقاله: 875 تعداد دریافت فایل اصل مقاله: 490 |
||