References
1. Donate, J.P., Sanchez, G.G., and De Miguel, A.S.
Time series forecasting. a comparative study between
an evolving articial neural networks system and statistical
methods", Int. J. Artif. Intell. Tools, 21(01),
1250010 (2012).
2. Tseng, C.-H., Cheng, S.-T., Wang, Y.-H., and Peng,
J.-T. Articial neural network model of the hybrid
EGARCH volatility of the Taiwan stock index option
prices", Physica A, 387(13), pp. 3192-3200 (2008).
3. Yu, H.-K. Weighted fuzzy time series models for
TAIEX forecasting", Physica A, 349(3-4), pp. 609-624
(2005).
4. Cheng, C.-H., Chen, T.-L., Teoh, H.J., and Chiang, C.-
H. Fuzzy time-series based on adaptive expectation
model for TAIEX forecasting", Expert Syst. Appl.,
34(2), pp. 1126-1132 (2008).
5. Leu, Y., Lee, C.-P., and Jou, Y.-Z. A distance-based
fuzzy time series model for exchange rates forecasting",
Expert Syst. Appl., 36(4), pp. 8107-8114 (2009).
6. Lee, C.-P., Lin, W.-C., and Yang, C.-C. A strategy
for forecasting option price using fuzzy time series and
least square support vector regression with a bootstrap
model", Sci. Iran., 21(3), pp. 815-825 (2014).
7. Leu, Y., Lee, C.-P., and Hung, C.-C. A fuzzy time
series-based neural network approach to option price
forecasting", In Intelligent Information and Database
Systems, N. Nguyen, M. Le and J. Swiatek, pp. 360-
369, Springer Berlin Heidelberg (2010).
8. Yang, C.-C., Leu, Y., and Lee, C.-P. A dynamic
weighted distanced-based fuzzy time series neural network
with bootstrap model for option price forecasting",
Rom. J. Econ. Forecast, 2014(2), pp. 115-129
(2014).
9. Lee, L.-W., Wang, L.-H., Chen, S.-M., and Leu, Y.-H.
Handling forecasting problems based on two-factors
high-order fuzzy time series", IEEE Trans. Fuzzy Syst.,
14(3), pp. 468-477 (2006).
10. Chen, S.-M. Forecasting enrollments based on highorder
fuzzy time series", Cybern. Syst., 33(1), pp. 1-16
(2002).
11. Liang, X., Zhang, H., and Li, X. A simple method of
forecasting option prices based on neural networks", In
Next-Generation Applied Intelligence, B.-C. Chien, T.-
P. Hong, S.-M. Chen, and M. Ali, pp. 586-593, Springer
Berlin Heidelberg (2009).
12. Huang, C.-F. A hybrid stock selection model using
genetic algorithms and support vector regression",
Appl. Soft. Comput., 12(2), pp. 807-818 (2012).
13. Wang, Y.-H. Nonlinear neural network forecasting
model for stock index option price: hybrid GJRGARCH
approach", Expert Syst. Appl., 36(1), pp. 564-
570 (2009).
1640 C.-H. Huang et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 1629{1640
14. Panda, C. and Narasimhan, V. Forecasting exchange
rate better with articial neural network", J. Policy
Model., 29(2), pp. 227-236 (2007).
15. Wei, L.-Y., Cheng, C.-H., and Wu, H.-H. A hybrid
ANFIS based on n-period moving average model to
forecast TAIEX stock", Appl. Soft. Comput., 19, pp.
86-92 (2014).
16. Wang, Y., Wang, B., and Zhang, X. A new application
of the support vector regression on the construction
of nancial conditions index to CPI prediction",
Procedia Comput. Sci., 9, pp. 1263-1272 (2012).
17. Chen, S.-M. and Kao, P.-Y. TAIEX forecasting based
on fuzzy time series, particle swarm optimization
techniques and support vector machines", Inf. Sci.,
247, pp. 62-71 (2013).
18. Chen, S.-M. Forecasting enrollments based on fuzzy
time series", Fuzzy Sets Syst., 81(3), pp. 311-319
(1996).
19. Song, Q. and Chissom, B.S. Forecasting enrollments
with fuzzy time series - Part I", Fuzzy Sets Syst., 54(1),
pp. 1-9 (1993).
20. Song, Q. and Chissom, B.S. Forecasting enrollments
with fuzzy time series-part II", Fuzzy Sets Syst., 62(1),
pp. 1-8 (1994).
21. McCulloch, W.S. and Pitts, W. A logical calculus of
the ideas immanent in nervous activity", Bull. Math.
Biol., 5(4), pp. 115-133 (1943).
22. Lee, C.-P., Shieh, G.-J., Yiu, T.-J., and Kuo, B.-J.
The strategy to simulate the cross-pollination rate for
the co-existence of genetically modied (GM) and non-
GM crops by using FPSOSVR", Chemometrics Intell.
Lab. Syst., 122, pp. 50-57 (2013).
23. Drucker, H., Burges, C.J.C., Kaufman, L., Smola,
A.J., and Vapnik, V. Support vector regression machines",
NIPS'1996, pp. 155-161 (1996).
24. Basak, D., Pal, S., and Patranabis, D.C. Support
vector regression", Neural Inf. Process Lett. Rev., 11,
pp. 203-224 (2007).
25. Kapoor, P. and Bedi, S.S. Weather forecasting using
sliding window algorithm", ISRN Signal Processing,
2013, p. 5 (2013).
26. Smola, A.J. and Scholkopf, B. A tutorial on support
vector regression", Stat. Comput., 14(3), pp. 199-222
(2004).
27. Kleinpeter, M.A. Multivariable analysis: A practical
guide for clinicians", 2nd Edn., J. Natl. Med. Assoc.,
99(6), pp. 684-684 (2007).
28. Ahlburg, D.A. How accurate are the U.S. bureau
of the census projections of total live births?", J.
Forecast., 1(4), pp. 365-374 (1982).
29. Ou, S.-L. Forecasting agricultural output with an
improved grey forecasting model based on the genetic
algorithm", Comput. Electron. Agric., 85, pp. 33-39
(2012).
30. Coshall, J.T. Combining volatility and smoothing
forecasts of UK demand for international tourism",
Tourism Manage., 30(4), pp. 495-511 (2009).
31. Gani, A., Mohammadi, K., Shamshirband, S., Altameem,
T.A., Petkovic, D., and Ch, S. A combined
method to estimate wind speed distribution based on
integrating the support vector machine with re
y
algorithm", Environ. Prog. Sustain. Energy, 35(3), pp.
867-875 (2016).
32. Hadavandi, E., Shahrabi, J., and Shamshirband, S. A
novel Boosted-neural network ensemble for modeling
multi-target regression problems", Eng. Appl. Artif.
Intell., 45, pp. 204-219 (2015).