A review on outlier/anomaly detection in time series data
Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …
enabling a large amount of data to be gathered over time and thus generating time series …
A survey of methods for time series change point detection
Change points are abrupt variations in time series data. Such abrupt changes may represent
transitions that occur between states. Detection of change points is useful in modelling and …
transitions that occur between states. Detection of change points is useful in modelling and …
Anomaly detection in univariate time-series: A survey on the state-of-the-art
M Braei, S Wagner - arxiv preprint arxiv:2004.00433, 2020 - arxiv.org
Anomaly detection for time-series data has been an important research field for a long time.
Seminal work on anomaly detection methods has been focussing on statistical approaches …
Seminal work on anomaly detection methods has been focussing on statistical approaches …
Time-series clustering–a decade review
Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …
about classes. With emerging new concepts like cloud computing and big data and their vast …
k-shape: Efficient and accurate clustering of time series
The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …
substantial interest in the analysis and mining of time series. Clustering is one of the most …
Toeplitz inverse covariance-based clustering of multivariate time series data
Subsequence clustering of multivariate time series is a useful tool for discovering repeated
patterns in temporal data. Once these patterns have been discovered, seemingly …
patterns in temporal data. Once these patterns have been discovered, seemingly …
Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison
Modeling time-varying operations in complex energy systems optimization problems is often
computationally intractable, and time-series input data are thus often aggregated to …
computationally intractable, and time-series input data are thus often aggregated to …
Time-series data mining
P Esling, C Agon - ACM Computing Surveys (CSUR), 2012 - dl.acm.org
In almost every scientific field, measurements are performed over time. These observations
lead to a collection of organized data called time series. The purpose of time-series data …
lead to a collection of organized data called time series. The purpose of time-series data …
A review on time series data mining
T Fu - Engineering Applications of Artificial Intelligence, 2011 - Elsevier
Time series is an important class of temporal data objects and it can be easily obtained from
scientific and financial applications. A time series is a collection of observations made …
scientific and financial applications. A time series is a collection of observations made …
[書籍][B] Machine learning and security: Protecting systems with data and algorithms
C Chio, D Freeman - 2018 - books.google.com
Can machine learning techniques solve our computer security problems and finally put an
end to the cat-and-mouse game between attackers and defenders? Or is this hope merely …
end to the cat-and-mouse game between attackers and defenders? Or is this hope merely …