document: On the need for time series data mining benchmarks : a survey and empirical demonstration In the last decade there has been an explosion of interest in mining time series data . Literally hundreds of papers have introduced new algorithms to index , classify , cluster and segment time series . In this work we make the following claim . Much of this work has very little utility because the contribution made ( speed in the case of indexing , accuracy in the case of classification and clustering , model accuracy in the case of segmentation ) offer an amount of `` improvement '' that would have been completely dwarfed by the variance that would have been observed by testing on many real world datasets , or the variance that would have been observed by changing minor ( unstated ) implementation details . To illustrate our point , we have undertaken the most exhaustive set of time series experiments ever attempted , re-implementing the contribution of more than two dozen papers , and testing them on 50 real world , highly diverse datasets . Our empirical results strongly support our assertion , and suggest the need for a set of time series benchmarks and more careful empirical evaluation in the data mining community .
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