Entropy-based symbolic representation for time series classification

In order to improve the performance of time-series classification, we introduce a new approach of time series classification. The first basic idea of the approach is to use entropy impurity measure to discretize and symbolize time series, which discretize the original time series into disjoint intervals using entropy impurity measure and then transform the time series into symbolic representations. The second idea of the approach is to combine symbolic representation of time series and k nearest neighbor to classify time series. The proposed approach is compared with a number of known pattern classifiers by benchmarking with the use of artificial and real-world data sets. The experimental results show it can reduce the error rates of time series classification, so it is highly competitive with previous approaches.