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The aim of event detection in time series is to identify particular occurrences of user-interest in one or more time lines, such as finding an anomaly in electrocardiograms or reporting a sudden variation of voltage in a power supply. Current methods are not adequate for detecting certain kinds of events without any domain knowledge. Therefore, we propose a Genetic Programming (GP) based event detection methodology in which solutions can be built from raw time series data. The framework is applied to five synthetic data sets and one real world application. The experimental results show that working on raw data even with a dimensionality as high as 140 × 80, genetic programming can achieve superior performance to conventional methods operating on pre-defined features. Furthermore, analysis of the evolved event detectors shows that they have captured the regularities inserted into the synthetic data sets and some individuals can be readily understood by humans.