Evolved Kernel Method for Time Series.

Cuevas-Tello, J.C.
Mexican International Conference on Artificial Intelligence, Lecture Notes in Artificial Intelligence (LNAI 4827), Springer-Verlag Berlin Heidelberg, pp. 559-569, 2007.


An evolutionary algorithm for parameter estimation of a ker- nel method for noisy and irregularly sampled time series is presented. We aim to estimate the time delay between time series coming from grav- itational lensing in astronomy. The parameters to estimate include the delay, the width kernels or smoothing, and a regularisation parameter. We use mixed types to represent variables within the evolutionary algo- rithm. The algorithm is tested on several artificial data sets, and also on real astronomical observations. The performance of our method is compared with the most popular methods for time delay estimation. An statistical analysis of results is given, where the results of our approach are more accurate and significant than those of other methods.