Genetic Programming Prediction of Solar Activity

Authors:
Romuald Jagielski
Affiliations:
-
Venue:
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Year:
2000

Citing 2
Cited 1

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Neural, Novel and Hybrid Algorithms for Time Series Prediction

Neural, Novel and Hybrid Algorithms for Time Series Prediction

Long memory time series forecasting by using genetic programming

Genetic Programming and Evolvable Machines

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Abstract

For many practical applications, such as planning for satellite orbits and space missions, it is important to estimate the future values of the sunspot numbers. There have been numerous methods used for this particular case of time series prediction, including recently neural networks. In this paper we present genetic programming technique employed to sunspot series prediction. The paper investigates practical solutions and heuristics for an effective choice of parameters and functions of genetic programming. The results obtained expect the maximum in the current cycle of the smoothed series monthly sunspot numbers is 164-20, and 162-20 for the next cycle maximum, at the 95% level of confidence. These results are discussed and compared with other predictions.