A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
SN Applied Sciences
This paper introduces a two-stage deep learning-based methodology for clustering time series data. First, a novel technique is introduced to utilize the characteristics (e.g., volatility) of the given time series data in order to create labels and thus enable transformation of the problem from an unsupervised into a supervised learning. Second, an autoencoderbased deep learning model is built to model both known and hidden non-linear features of time series data. The paper reports a case studydoi:10.1007/s42452-020-2584-8 fatcat:ebtmxxqftzbo7bbm3abpju43sm