Elsevier

IFAC Proceedings Volumes

Volume 31, Issue 16, June–July 1998, Pages 275-279
IFAC Proceedings Volumes

Statistical Evaluation of Symbolic Regression Forecasting of Time-Series

https://doi.org/10.1016/S1474-6670(17)40494-0Get rights and content

Abstract

This is an evaluation of the ability of symbolic regression to predict timeseries. Symbolic regression is an application of genetic programming. Three codes GPCPP, GPQuick, and Vienna University GP Kemel-written in C++ were tested. Six models generated data by linear, nonlinear, and pseudo-random processes, and the three codes were employed to search for the six data generating processes. The results suggest that: (1) complexity and predictability are inversely related, (2) the symbolic regression technique is successful in predicting less complex processes, and (3) all three failed to find a data generating process for pseudo-nmdom data.

Keywords

Genetic Programming
nonlinear dynamics
complexity
artificialintelligence

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