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Huang Wei1; Nakamori Yoshiteru1; Wang Shouyang2
Source Publicationjournalofsystemsscienceandcomplexity
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.
Document Type期刊论文
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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