Cluster-based stability evaluation in time series data sets
In modern data analysis, time is often considered just another feature. Yet time has a special
role that is regularly overlooked. Procedures are usually only designed for time-independent …
role that is regularly overlooked. Procedures are usually only designed for time-independent …
Detection of causally anomalous time-series
Many complex and important real-life applications, such as surveillance, monitoring and
fraud detection, need to identify entire time-series, from a given collection, as anomalous. In …
fraud detection, need to identify entire time-series, from a given collection, as anomalous. In …
Fuzzy clustering stability evaluation of time series
The discovery of knowledge by analyzing time series is an important field of research. In this
paper we investigate multiple multivariate time series, because we assume a higher …
paper we investigate multiple multivariate time series, because we assume a higher …
[PDF][PDF] Towards Extracting Causal Graph Structures from TradeData and Smart Financial Portfolio Risk Management.
P Ravivanpong, T Riedel, P Stock - EDBT/ICDT Workshops, 2022 - ceur-ws.org
Risk managers of asset management companies monitor portfolio risk metrics such as the
Value at Risk in order to analyze and to communicate the risks timely to portfolio managers …
Value at Risk in order to analyze and to communicate the risks timely to portfolio managers …
[PDF][PDF] How is your team spirit? Cluster over-time stability evaluation
Clustering of time series data is a major part of data mining. In this paper, we consider
multiple multivariate time series and the clustering of their data points per timestamp. One of …
multiple multivariate time series and the clustering of their data points per timestamp. One of …
Behave or be detected! Identifying outlier sequences by their group cohesion
Since the amount of sequentially recorded data is constantly increasing, the analysis of time
series (TS), and especially the identification of anomalous points and subsequences, is …
series (TS), and especially the identification of anomalous points and subsequences, is …
Loners stand out. Identification of anomalous subsequences based on group performance
Time series analysis is a part of data mining and nowadays an important field of research
due to the increasing amount of data that is recorded sequentially by various systems …
due to the increasing amount of data that is recorded sequentially by various systems …
Clustering of time series regarding their over-time stability
The clustering of time series data is still a challenging task. There are different approaches
which consider either multiple time series or a single one. While some interpret the whole …
which consider either multiple time series or a single one. While some interpret the whole …
Alone We Can Do So Little; Together We Cannot Be Detected
It is no longer possible to imagine our everyday life without time series data. This includes,
for example, market developments, COVID-19 cases, electricity prices, and other data from a …
for example, market developments, COVID-19 cases, electricity prices, and other data from a …
[PDF][PDF] Alone We Can Do So Little; Together We Cannot Be Detected. Eng. Proc. 2022, 18, 3
S Korlakov, G Klassen, M Bravidor, S Conrad - 2022 - researchgate.net
It is no longer possible to imagine our everyday life without time series data. This includes,
for example, market developments, COVID-19 cases, electricity prices, and other data from a …
for example, market developments, COVID-19 cases, electricity prices, and other data from a …