Predictive change point detection for heterogeneous data
This work addresses the need for more accurate change point detection in sequential data analysis, particularly for applications like tribological monitoring, though it appears incremental as it builds on existing CPD methods with enhanced predictive modeling.
The paper tackles the problem of change point detection in heterogeneous data by introducing a 'Predict and Compare' framework that uses predictive models like ARIMA and LSTM to improve standard methods, outperforming state-of-the-art online CPD routines in terms of false positive rate and out-of-control average run length.
A change point detection (CPD) framework assisted by a predictive machine learning model called "Predict and Compare" is introduced and characterised in relation to other state-of-the-art online CPD routines which it outperforms in terms of false positive rate and out-of-control average run length. The method's focus is on improving standard methods from sequential analysis such as the CUSUM rule in terms of these quality measures. This is achieved by replacing typically used trend estimation functionals such as the running mean with more sophisticated predictive models (Predict step), and comparing their prognosis with actual data (Compare step). The two models used in the Predict step are the ARIMA model and the LSTM recursive neural network. However, the framework is formulated in general terms, so as to allow the use of other prediction or comparison methods than those tested here. The power of the method is demonstrated in a tribological case study in which change points separating the run-in, steady-state, and divergent wear phases are detected in the regime of very few false positives.