Time series change point detection with self-supervised contrastive predictive coding
Change Point Detection (CPD) methods identify the times associated with changes in the
trends and properties of time series data in order to describe the underlying behaviour of the …
trends and properties of time series data in order to describe the underlying behaviour of the …
[HTML][HTML] Lifting hospital electronic health record data treasures: challenges and opportunities
Electronic health records (EHRs) have been successfully used in data science and machine
learning projects. However, most of these data are collected for clinical use rather than for …
learning projects. However, most of these data are collected for clinical use rather than for …
Real-time change-point detection: A deep neural network-based adaptive approach for detecting changes in multivariate time series data
The behavior of a time series may be affected by various factors. Changes in mean,
variance, frequency, and auto-correlation are the most common. Change-Point Detection …
variance, frequency, and auto-correlation are the most common. Change-Point Detection …
Tipping point detection using reservoir computing
Detection in high fidelity of tipping points, the emergence of which is often induced by
invisible changes in internal structures or/and external interferences, is paramountly …
invisible changes in internal structures or/and external interferences, is paramountly …
Energy demand forecasting using adaptive ARFIMA based on a novel dynamic structural break detection framework
A Nikseresht, H Amindavar - Applied Energy, 2024 - Elsevier
Forecasting energy demand has become increasingly important due to technological
advances, especially new power systems and population growth. Accurate predictions of …
advances, especially new power systems and population growth. Accurate predictions of …
Multi-view change point detection in dynamic networks
Change point detection aims to find the locations of sudden changes in the network
structure, which persist with time. However, most current methods usually focus on how to …
structure, which persist with time. However, most current methods usually focus on how to …
A self-supervised contrastive change point detection method for industrial time series
X Bao, L Chen, J Zhong, D Wu, Y Zheng - Engineering Applications of …, 2024 - Elsevier
Manufacturing process monitoring is crucial to ensure production quality. This paper
formulates the detection problem of abnormal changes in the manufacturing process as the …
formulates the detection problem of abnormal changes in the manufacturing process as the …
[HTML][HTML] PrecTime: A deep learning architecture for precise time series segmentation in industrial manufacturing operations
S Gaugel, M Reichert - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
The fourth industrial revolution creates ubiquitous sensor data in production plants. To
generate maximum value out of these data, reliable and precise time series-based machine …
generate maximum value out of these data, reliable and precise time series-based machine …
A semi-supervised interactive algorithm for change point detection
The goal of change point detection (CPD) is to identify abrupt changes in the statistics of
signals or time series that reflect transitions in the underlying system's properties or states …
signals or time series that reflect transitions in the underlying system's properties or states …
A boundary consistency-aware multitask learning framework for joint activity segmentation and recognition with wearable sensors
With the development of industrial and sensing technology, sensor-based activity
recognition has become a promising technology for informatics applications. However, in a …
recognition has become a promising technology for informatics applications. However, in a …