In this paper, the use of an evolutionary approach when obtaining linguistic summaries from time series data is proposed. We assume the availability of a hierarchical partition of the time dimension in the time series. The use of natural language allows the human users to understand the resulting summaries in an easy way. The number of possible final summaries and the different ways of measuring their quality has taken us to adopt the use of a multi objective evolutionary algorithm. We compare the results of the new approach with our previous greedy algorithms.
Other work includes the use of genetic algorithms REF to generate linguistic summaries from time-series, and those that place emphasis
Linguistic Summarization of Time Series Data using Genetic Algorithms
EUSFLAT Conf.
Computer Science