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ageneralapproachbasedonautocorrelationtodetermineinputvariablesofneuralnetworksfortimeseriesforecasting
Huang Wei1; Nakamori Yoshiteru1; Wang Shouyang2
2004
Source Publicationjournalofsystemsscienceandcomplexity
ISSN1009-6124
Volume017Issue:003Pages:297
AbstractInput selection is probably one of the most critical decision issues in neural network designing, because it has a great impact on forecasting performance. Among the many applications of artificial neural networks to finance, time series forecasting is perhaps one of the most challenging issues. Considering the features of neural networks, we propose a general approach called Autocorrelation Criterion (AC) to determine the inputs variables for a neural network. The purpose is to seek optimal lag periods, which are more predictive and less correlated. AC is a data-driven approach in that there is no prior assumptiona bout the models for time series under study. So it has extensive applications and avoids a lengthy experimentation and tinkering in input selection. We apply the approach to the determination of input variables for foreign exchange rate forecasting and conductcomparisons between AC and information-based in-sample model selection criterion. The experiment results show that AC outperforms information-based in-sample model selection criterion.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/41206
Collection系统科学研究所
Affiliation1.北陆先端科学技术大学院大学
2.中国科学院数学与系统科学研究院
Recommended Citation
GB/T 7714
Huang Wei,Nakamori Yoshiteru,Wang Shouyang. ageneralapproachbasedonautocorrelationtodetermineinputvariablesofneuralnetworksfortimeseriesforecasting[J]. journalofsystemsscienceandcomplexity,2004,017(003):297.
APA Huang Wei,Nakamori Yoshiteru,&Wang Shouyang.(2004).ageneralapproachbasedonautocorrelationtodetermineinputvariablesofneuralnetworksfortimeseriesforecasting.journalofsystemsscienceandcomplexity,017(003),297.
MLA Huang Wei,et al."ageneralapproachbasedonautocorrelationtodetermineinputvariablesofneuralnetworksfortimeseriesforecasting".journalofsystemsscienceandcomplexity 017.003(2004):297.
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