Forecasting time series with optimal neural networks using multi-objective optimization algorithm based on AICc

Muzhou HOU, Yunlei YANG, Taohua LIU, Wenping PENG

PDF(304 KB)
PDF(304 KB)
Front. Comput. Sci. ›› 2018, Vol. 12 ›› Issue (6) : 1261-1263. DOI: 10.1007/s11704-018-8095-8
LETTER

Forecasting time series with optimal neural networks using multi-objective optimization algorithm based on AICc

Author information +
History +

Cite this article

Download citation ▾
Muzhou HOU, Yunlei YANG, Taohua LIU, Wenping PENG. Forecasting time series with optimal neural networks using multi-objective optimization algorithm based on AICc. Front. Comput. Sci., 2018, 12(6): 1261‒1263 https://doi.org/10.1007/s11704-018-8095-8

References

[1]
Zevallos M, Santos B, Hotta L K. A note on influence diagnostics in AR(1) time series models. Journal of Statistical Planning and Inference, 2012, 142(11): 2999–3007
CrossRef Google scholar
[2]
Cabaña A, Scavino M. Weak convergence of marked empirical processes for focused inference on AR(p) vsAR(p+1) stationary time series. Methodology & Computing in Applied Probability, 2012, 14(3): 793–810
CrossRef Google scholar
[3]
Zhao Z, Zhang Y, Liao H. Design of ensemble neural network using the Akaike information criterion. Engineering Applications of Artificial Intelligence, 2008, 21(8): 1182–1188
CrossRef Google scholar
[4]
Sugiura N. Further analysts of the data by Akaike’s information criterion and the finite corrections. Communications in Statistics-Theory and Methods, 1978, 7(1): 13–26
CrossRef Google scholar
[5]
Xi L, Hou M, Lee M H, Li J, Wei D, Hai H, Wu Y. A new constructive neural network method for noise processing and its application on stock market prediction. Applied Soft Computing Journal, 2014, 15(2): 57–66
CrossRef Google scholar

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
AI Summary AI Mindmap
PDF(304 KB)

Accesses

Citations

Detail

Sections
Recommended

/