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- Title
Time series forecasting with genetic programming.
- Authors
Graff, Mario; Tellez, Eric; Escalante, Hugo; Ornelas-Tellez, Fernando
- Abstract
Genetic programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard world problems. There has been a lot of interest in using GP to tackle forecasting problems. Unfortunately, it is not clear whether GP can outperform traditional forecasting techniques such as auto-regressive models. In this contribution, we present a comparison between standard GP systems qand auto-regressive integrated moving average model and exponential smoothing. This comparison points out particular configurations of GP that are competitive against these forecasting techniques. In addition to this, we propose a novel technique to select a forecaster from a collection of predictions made by different GP systems. The result shows that this selection scheme is competitive with traditional forecasting techniques, and, in a number of cases it is statistically better.
- Subjects
GENERIC programming (Computer science); TIME series analysis; ARTIFICIAL neural networks; BACK propagation; ALGORITHMS
- Publication
Natural Computing, 2017, Vol 16, Issue 1, p165
- ISSN
1567-7818
- Publication type
Article
- DOI
10.1007/s11047-015-9536-z