asMODiTS: An application of surrogate models to optimize Time Series symbolic discretization through archive-based training set update strategy

Abstract

The enhanced multi-objective symbolic discretization for time series (eMODiTS) uses an evolutionary process to identify the appropriate discretization scheme in the Time Series Classification (TSC) task. It discretizes using a unique alphabet cut for each word segment. However, this kind of scheme has a higher computational cost. Therefore, this study implemented surrogate models to minimize this cost. The general procedure is summarized below. • The K-nearest neighbor for regression, the support vector regression model, and the Ra- dial Basis Functions neural networks were implemented as surrogate models to estimate the objective values of eMODiTS, including the discretization process. • An archive-based update strategy was introduced to maintain diversity in the training set. • Finally, the model update process uses a hybrid (fixed and dynamic) approach for the surrogate model's evolution control.

Keywords

• Archive-based and surrogate-assisted multi-objective approach for symbolic discretization of time series (asMODiTS)