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. .

时间序列分类是时间序列挖掘中最重要的任务之一，本论文介绍了一种以效率为主要目标的新方法，通过简化时间序列的表示和考虑部分值的距离度量，实现对大型时间序列数据集的高效分类。实验结果表明，与其他高效方法相比，该方法不仅平均速度快 4 倍，而且在分类准确性上更为优越。