| A GENERAL APPROACH BASED ON AUTOCORRELATION TO DETERMINE INPUT VARIABLES OF NEURAL NETWORKS FOR TIME SERIES FORECASTING |
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| 引用本文： | HUANGWei NAKAMORIYoshiteru WANGShouyang. A GENERAL APPROACH BASED ON AUTOCORRELATION TO DETERMINE INPUT VARIABLES OF NEURAL NETWORKS FOR TIME SERIES FORECASTING[J]. 系统科学与复杂性, 2004, 17(3): 297-305 |
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| 作者姓名： | HUANGWei NAKAMORIYoshiteru WANGShouyang |
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| 作者单位： | School of Knowledge Science，Japan Advanced Institute of Science and Technology，Asahidai 1-1，Tatsunokuchi，Ishikawa 923-1292，Japan，School of Knowledge Science，Japan Advanced Institute of Science and Technology，Asahidai 1-1，Tatsunokuchi，Ishikawa 923-1292，Japan |
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| 基金项目： | This research is partially supported by Chinese Academy of Sciences，National Science Foundation of China，Japan Society for the Promotion of Science. |
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| 摘 要： | Input 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 assumption about 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 conduct comparisons between AC and information-based in-sample model selection criterion. The experiment results show that AC outperforms inf
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| 关 键 词： | 时间序列预测 人工神经网络 输入变量 外部交换速率 自相关标准 |
A GENERAL APPROACH BASED ON AUTOCORRELATION TO DETERMINE INPUT VARIABLES OF NEURAL NETWORKS FOR TIME SERIES FORECASTING |
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| HUANG Wei. A GENERAL APPROACH BASED ON AUTOCORRELATION TO DETERMINE INPUT VARIABLES OF NEURAL NETWORKS FOR TIME SERIES FORECASTING[J]. Journal of Systems Science and Complexity, 2004, 17(3): 297-305 |
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| Authors: | HUANG Wei |
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| Abstract: | Input 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 assumption about 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 conduct comparisons between AC and information-based in-sample model selection criterion. The experiment results show that AC outperforms information-based in-sample model selection criterion. |
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| Keywords: | Input variables foreign exchange rate neural networks time series forecasting |
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