Unsupervised Change Point Detection for heterogeneous sensor signals
This work addresses the problem of algorithm selection for time series analysis in domains like sensor monitoring, but it appears incremental as it focuses on comparing existing unsupervised techniques without introducing new methods.
The paper tackles the challenge of selecting appropriate unsupervised change point detection algorithms for heterogeneous sensor signals by introducing and evaluating methods based on several criteria, but it does not report specific results or concrete numbers.
Change point detection is a crucial aspect of analyzing time series data, as the presence of a change point indicates an abrupt and significant change in the process generating the data. While many algorithms for the problem of change point detection have been developed over time, it can be challenging to select the appropriate algorithm for a specific problem. The choice of the algorithm heavily depends on the nature of the problem and the underlying data source. In this paper, we will exclusively examine unsupervised techniques due to their flexibility in the application to various data sources without the requirement for abundant annotated training data and the re-calibration of the model. The examined methods will be introduced and evaluated based on several criteria to compare the algorithms.