Tapnet: Multivariate time series classification with attentional prototypical network
With the advance of sensor technologies, the Multivariate Time Series classification (MTSC)
problem, perhaps one of the most essential problems in the time series data mining domain …
problem, perhaps one of the most essential problems in the time series data mining domain …
Persistence-based motif discovery in time series
Motif Discovery consists of finding repeated patterns and locating their occurrences in a time
series without prior knowledge about their shape or location. Most state-of-the-art algorithms …
series without prior knowledge about their shape or location. Most state-of-the-art algorithms …
TCRAN: Multivariate time series classification using residual channel attention networks with time correction
H Zhu, J Zhang, H Cui, K Wang, Q Tang - Applied Soft Computing, 2022 - Elsevier
Currently, the most popular and effective approach to solve multivariate time series
classification (MTSC) tasks is based on deep learning technology. However, the existing …
classification (MTSC) tasks is based on deep learning technology. However, the existing …
Multi-index ecoacoustics analysis for terrestrial soundscapes: a new semi-automated approach using time-series motif discovery and random forest classification
High rates of biodiversity loss caused by human-induced changes in the environment
require new methods for large scale fauna monitoring and data analysis. While ecoacoustic …
require new methods for large scale fauna monitoring and data analysis. While ecoacoustic …
[HTML][HTML] Enhanced NILM load pattern extraction via variable-length motif discovery
Due to the diversity of appliances and users' power consumption behaviors, it is challenging
to accurately extract load signature samples for non-intrusive load monitoring (NILM) in …
to accurately extract load signature samples for non-intrusive load monitoring (NILM) in …
HIME: discovering variable-length motifs in large-scale time series
Detecting repeated variable-length patterns, also called variable-length motifs, has received
a great amount of attention in recent years. The state-of-the-art algorithm utilizes a fixed …
a great amount of attention in recent years. The state-of-the-art algorithm utilizes a fixed …
Matrix profile XIX: time series semantic motifs: a new primitive for finding higher-level structure in time series
S Imani, E Keogh - 2019 IEEE International Conference on …, 2019 - ieeexplore.ieee.org
Time series motifs are approximately repeated patterns in real-valued temporal data. They
are used for exploratory data mining methods including clustering, classification …
are used for exploratory data mining methods including clustering, classification …
Parameter-free Spikelet: Discovering Different Length and Warped Time Series Motifs using an Adaptive Time Series Representation
M Imamura, T Nakamura - Proceedings of the 29th ACM SIGKDD …, 2023 - dl.acm.org
Over the last two decades, time series motif discovery has emerged as a useful primitive for
many downstream analytical tasks, including clustering, classification, rule discovery …
many downstream analytical tasks, including clustering, classification, rule discovery …
Discovering subdimensional motifs of different lengths in large-scale multivariate time series
Detecting repeating patterns of different lengths in time series, also called variable-length
motifs, has received a great amount of attention by researchers and practitioners. Despite …
motifs, has received a great amount of attention by researchers and practitioners. Despite …
Entropy-based symbolic aggregate approximation representation method for time series
H Zhang, Y Dong, D Xu - 2020 IEEE 9th Joint International …, 2020 - ieeexplore.ieee.org
Symbolic Aggregate approXimation (SAX) is one of the most common dimensionality
reduction approaches for time-series and has been widely employed in lots of domains …
reduction approaches for time-series and has been widely employed in lots of domains …