Online non-parametric changepoint detection with application to monitoring operational performance of network devices

Bayesian Online Changepoint Detection

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Her...

Equipment Health Monitoring with Non-Parametric Statistics for Online Early Detection and Scoring of Degradation

This paper develops a health monitoring scheme to detect and trend degradation in dynamic systems that are characterised by multiple parameter time-series data. The presented scheme provides early detection of degradation and ability to score its significance in order to inform maintenance planning and consequently reduce disruption. Non-parametric statistics are proposed to provide this early ...

Nonparametric Methods for Online Changepoint Detection

Changepoints have been extensively analysed in order to identify structural changes in time series data, typically when the data are of known parametric form. This report presents an exploration of methods to detect changepoints in a nonparametric setting, where no assumptions are made with regard to the distributional structure of the data, yet must still maintain a specified level of performa...

a new type-ii fuzzy logic based controller for non-linear dynamical systems with application to 3-psp parallel robot

abstract type-ii fuzzy logic has shown its superiority over traditional fuzzy logic when dealing with uncertainty. type-ii fuzzy logic controllers are however newer and more promising approaches that have been recently applied to various fields due to their significant contribution especially when the noise (as an important instance of uncertainty) emerges. during the design of type- i fuz...

Bayesian approach to multilayer stochastic blockmodel and network changepoint detection