Paper Info
Title
Hybridization Of Analytic Programming And Differential Evolution For Time Series Prediction
Abstract
This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of the hybrid connection between AP and different strategies of DE. AP can be considered as a powerful open framework for symbolic regression thanks to its usability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the applicability and differences in performance of AP driven by basic canonical strategy of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). An experiment with three case studies has been carried out here with the several time series consisting of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.
Year
DOI
Venue
2017
10.1007/978-3-319-59650-1_58
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2017
Keywords
Field
DocType
Analytic programming, Differential evolution, SHADE, Time series prediction
Time series,Swarm behaviour,Regression,Computer science,Usability,Differential evolution,Artificial intelligence,Analytic programming,Predictive modelling,Symbolic regression,Machine learning
Conference
Volume
ISSN
Citations 
10334
0302-9743
0
PageRank 
References 
Authors
0.34
14
5
Name
Order
Citations
PageRank
Roman Senkerik137574.92
Adam Viktorin22916.76
Michal Pluhacek321747.34
Tomas Kadavy42020.97
Ivan Zelinka545182.16