Bernstein polynomials for adaptive evolutionary prediction of short-term time series

被引：7

作者：

Lukoseviciute, Kristina ^{[1
]}

Baubliene, Rita ^{[1
]}

Howard, Daniel ^{[2
]}

Ragulskis, Minvydas ^{[1
]}

机构：

[1] Kaunas Univ Technol, Res Grp Math & Numer Anal Dynam Syst, Studentu 50-147, LT-51368 Kaunas, Lithuania

[2] Howard Sci Ltd, Malvern, Worcs, England

来源：

APPLIED SOFT COMPUTING | 2018年 / 65卷

关键词：

Bernstein polynomial; Time series prediction; Evolutionary algorithms; ARTIFICIAL NEURAL-NETWORKS; SMOOTH ESTIMATION; DENSITY-FUNCTION; MODELS; REGRESSION; ELECTRICITY; DEMAND;

D O I：

10.1016/j.asoc.2018.01.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce a short-term time series prediction model by means of evolutionary algorithms and Bernstein polynomials. This adapts Bernstein-type algebraic skeletons to extrapolate and predict short time series. A mixed smoothing strategy is used to achieve the necessary balance between the roughness of the algebraic prediction and the smoothness of the moving average. Computational experiments with standardized real world time series illustrate the accuracy of this approach to short-term prediction. (c) 2018 Elsevier B.V. All rights reserved.

引用

页码：47 / 57

页数：11

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