  Time series classification (TSC) is the most import task in time series
mining as it has several applications in medicine, meteorology, finance cyber
security, and many others. With the ever increasing size of time series
datasets, several traditional TSC methods are no longer efficient enough to
perform this task on such very large datasets. Yet, most recent papers on TSC
focus mainly on accuracy by using methods that apply deep learning, for
instance, which require extensive computational resources that cannot be
applied efficiently to very large datasets. The method we introduce in this
paper focuses on these very large time series datasets with the main objective
being efficiency. We achieve this through a simplified representation of the
time series. This in turn is enhanced by a distance measure that considers only
some of the values of the represented time series. The result of this
combination is a very efficient representation method for TSC. This has been
tested experimentally against another time series method that is particularly
popular for its efficiency. The experiments show that our method is not only 4
times faster, on average, but it is also superior in terms of classification
accuracy, as it gives better results on 24 out of the 29 tested time series
datasets. .
