Bibliographic Details
| Title: |
Time series forecasting with genetic programming. |
| Authors: |
Graff, Mario1, Tellez, Eric1, Escalante, Hugo2, Ornelas-Tellez, Fernando3 |
| Source: |
Natural Computing. Mar2017, Vol. 16 Issue 1, p165-174. 10p. |
| Subjects: |
Generic programming (Computer science), Time series analysis, Artificial neural networks, Back propagation, Algorithms |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
