Shape and time distortion loss for training deep time series forecasting models
This paper addresses the problem of time series forecasting for non-stationary signals and
multiple future steps prediction. To handle this challenging task, we introduce DILATE …
multiple future steps prediction. To handle this challenging task, we introduce DILATE …
A kernel multiple change-point algorithm via model selection
We consider a general formulation of the multiple change-point problem, in which the data is
assumed to belong to a set equipped with a positive semidefinite kernel. We propose a …
assumed to belong to a set equipped with a positive semidefinite kernel. We propose a …
A framework for robust hybrid state estimation with unknown measurement noise statistics
In practical applications like power systems, the distribution of the measurement noise is
usually unknown and frequently deviates from the assumed Gaussian model, yielding …
usually unknown and frequently deviates from the assumed Gaussian model, yielding …
Assessment of data suitability for machine prognosis using maximum mean discrepancy
As more and more data become available for machine prognostic analysis in the big data
environment, effective data suitability assessment methods become highly desired to help …
environment, effective data suitability assessment methods become highly desired to help …
Change point detection in time series data using autoencoders with a time-invariant representation
Change point detection (CPD) aims to locate abrupt property changes in time series data.
Recent CPD methods demonstrated the potential of using deep learning techniques, but …
Recent CPD methods demonstrated the potential of using deep learning techniques, but …
Kernel change-point detection with auxiliary deep generative models
Detecting the emergence of abrupt property changes in time series is a challenging
problem. Kernel two-sample test has been studied for this task which makes fewer …
problem. Kernel two-sample test has been studied for this task which makes fewer …
Similarity-based particle filter for remaining useful life prediction with enhanced performance
This paper proposes a similarity-based Particle Filter (PF) method for Remaining Useful Life
(RUL) prediction with improved performance. In the proposed methodology, Maximum Mean …
(RUL) prediction with improved performance. In the proposed methodology, Maximum Mean …
A similarity based methodology for machine prognostics by using kernel two sample test
This paper proposes a novel similarity-based algorithm for Remaining Useful Life (RUL)
prediction and a methodology for machine prognostics. In the proposed RUL prediction …
prediction and a methodology for machine prognostics. In the proposed RUL prediction …
Change point detection via multivariate singular spectrum analysis
The objective of change point detection (CPD) is to detect significant and abrupt changes in
the dynamics of the underlying system of interest through multivariate time series …
the dynamics of the underlying system of interest through multivariate time series …