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

Muzhou HOU; Yunlei YANG; Taohua LIU; Wenping PENG

doi:10.1007/s11704-018-8095-8

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

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

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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 DOI:10.1007/s11704-018-8095-8

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References

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[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

[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

[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

[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

[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