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Yu Lean1; Wang Shouyang1; Lai K K2; Nakamori Y3
Source Publicationjournalofsystemsscienceandcomplexity
AbstractVarious mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this study.The results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.
Document Type期刊论文
Recommended Citation
GB/T 7714
Yu Lean,Wang Shouyang,Lai K K,et al. timeseriesforecastingwithmultiplecandidatemodelsselectingorcombining[J]. journalofsystemsscienceandcomplexity,2005,018(001):1.
APA Yu Lean,Wang Shouyang,Lai K K,&Nakamori Y.(2005).timeseriesforecastingwithmultiplecandidatemodelsselectingorcombining.journalofsystemsscienceandcomplexity,018(001),1.
MLA Yu Lean,et al."timeseriesforecastingwithmultiplecandidatemodelsselectingorcombining".journalofsystemsscienceandcomplexity 018.001(2005):1.
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