Automated Linear Modeling of Time Series with Self Adaptive Genetic Algorithms

Two heuristic algorithms that automatically calculate linear expressions for time series (TS) are presented. The algorithms are based on the Box-Jenkins methodology in order to estimate the maximum number of terms of the linear expression and the intervals in which the series coefficients vary. With this information and establishing beforehand the number of terms that are required by the user, self adaptive genetic algorithms (SAGA) are applied in several stages of optimization to obtain the series model. It is worth to mention that these algorithms allow treating series with time-dependent trends and variance. In the paper the results of the application of SAGA to the NN3-reduced TS are also presented concluding that six of the eleven examples can be considered linear series. Regardless of the existence of papers where genetic algorithms are used in TS, it is important to mention that no reference of the use of SAGA in the area was found.