The UCR Time Series Archive - introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets. This paper introduces and will focus on the new data expansion from 85 to 128 data sets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline (1-nearest neighbor classification), a large fraction may be misattributing the reasons for their improvement. Moreover, they may have been able to achieve the same improvement with a
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The dataset provided is a collection of real-world industrial vibration data collected from a brownfield CNC milling machine. The acceleration has been measured using a tri-axial accelerometer (Bosch CISS Sensor) mounted inside the machine. The X- Y- and Z-axes of the accelerometer have been recorded using a sampling rate equal to 2 kHz. Thereby normal as well as anomalous data have been collected for 4 different timeframes, each lasting 5 months from February 2019 until August 2021 and labelled accordingly. It can be used to investigate the scalability of models and research process variations as the anomaly impact differs. In total there is data from three different CNC milling machines each executing 15 processes. For a detailed description of the data and experimental set-up, please refer to the paper: https://doi.org/10.1016/j.procir.2022.04.022
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The data used for all results in this paper can be found here. This directory contains: