基于时间序列预测的独立分量排序

独立分量排序是独立分量分析的热点问题,是提高特征空间鲁棒性和减少计算复杂度的必要前提。结合ICA在时间序列预测的应用,给出了基于一阶差分和最小方差误差的多分量联合重构预测排序准则。为了避免联合优化中出现的海量计算问题,提出了添加-测试-接受机制(ATA)的次优搜索方法。实验结果表明,和传统排序方法比较,新方法具有优异的预测能力和搜索效率。

Independent Component Ordering in Time Series Forecasting

The ordering of independent components is a hot issue in independent component analysis(ICA),and the critical step for the robustness and computing complexity of independent feature space.In time series forecasting,a novel criterion has been presented based on the mechanism of first-order differential and minimum variance error under multiple components reconstruction.To avoid the exhaustive search in combinatorial optimization,a sub-optimum approach named Adding-Testing-Acceptance(ATA) is proposed.Experimental results show that the proposed method has a better forecasting ability and more efficient in comparison with the existing ones.