Time Series Prediction Using Deterministic Geometric Semantic Genetic Programming

Predicting time series data is one of the most important challenges in many different application domains. Constructing the prediction models can be regarded as symbolic regressions, and the model can be optimized by Genetic Programming (GP), which is an evolutionary automatic programming method for...

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Bibliographic Details
Main Authors: Hara, Akira, Kushida, Jun-ichi, Takahama, Tetsuyuki
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Genetic programming
Predictive models
Semantics
Time series analysis
Training data
Online Access:Request full text
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Summary:Predicting time series data is one of the most important challenges in many different application domains. Constructing the prediction models can be regarded as symbolic regressions, and the model can be optimized by Genetic Programming (GP), which is an evolutionary automatic programming method for tree structural programs. In the last decade, semantics-based genetic operators have attracted much attentions for improving search performance in the field of GP. As one of the semantics-based GP, we have previously proposed Deterministic Geometric Semantic GP (D-GSGP). Crossover operations in D-GSGP generate offspring by affine combinations of parents with the optimal combination ratios. We have shown the effectiveness in several benchmark functions in symbolic regression problems. In this research, we apply the method to a time-series forecasting problem, sunspot number series, as more practical application. The experimental results indicate that D-GSGP works effectively and the acquired programs are useful for knowledge acquisition of the application domain.
ISSN:2577-1655
DOI:10.1109/SMC.2019.8914562