Motifs discovery for streaming time series
Qi Zhang, Yang Gao, Jiecai Zheng, Lin Chen, Xueqing Li · 2016
The motif discovery approach is used to measure the correlation of the pair of consecutiveness in time series, which also aims to find all subsequences which are similar to the given one.However, along with the arrival of Industry 4.0 era, massive numbers of detecting instruments in various fields are continuously producing a plenty number of time series streaming data, the high dimensionality and continuousness of streaming time series give rise to the potential threat for searchingeffectiveness.For these reasons, we come up with a novel motifs discovery approach for streaming time series based on piecewise linear representation with turningpoints and skyline index.As the experimental results suggest, our approach is more effective than some other traditional methods.