Short-term time series algebraic forecasting with internal smoothing

被引：0

作者：

Palivonaite, Rita ^{[1
]}

Ragulskis, Minvydas ^{[2
]}

机构：

[1] Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-325, Kaunas LT-51368, Lithuania

[2] Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-222, Kaunas LT-51368, Lithuania

来源：

Neurocomputing | 2014年 / 127卷

关键词：

Forecasting - Evolutionary algorithms - Matrix algebra - Numerical methods;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A new algebraic forecasting method with internal smoothing is proposed for short-term time series prediction. The concept of the H-rank of a sequence is exploited for the detection of a base algebraic fragment of the time series. Evolutionary algorithms are exploited for the identification of the set of corrections which are used to perturb the original time series. The proposed forecasting method is constructed to find a near-optimal balance between the variability of algebraic predictors and the smoothness of averaging methods. Numerical experiments with an artificially generated and real-world time series are used to illustrate the potential of the proposed method.© 2013 Elsevier B.V.

引用

页码：161 / 171