Clustering of Time Series Regarding Their Over-Time Stability

Klassen, Gerhard; Tatusch, Martha; Conrad, Stefan

doi:10.1109/ssci47803.2020.9308516

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apart from the presented ones, further methods based on CLOSE may be developed, e.g. an over-time clustering algorithm [29] or the prediction of the further course of sequences or clusters. Similar subsequences and patterns may already be identified by investigating the resulting clusters.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Cluster-based stability evaluation in time series data sets

2022

Self Cite

View full text Add to dashboard Cite

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 data and are therefore often unsuitable for the temporal aspect of the data. This is especially the case for clustering algorithms. Although there are a few evolutionary approaches for time-dependent data, the evaluation of these and therefore the selection is difficult for the user. In this paper, we present a general evaluation measure that examines clusterings with respect to their temporal stability and thus provides information about the achieved quality. For this purpose, we examine the temporal stability of time series with respect to their cluster neighbors, the temporal stability of clusters with respect to their composition, and finally conclude on the temporal stability of the entire clustering. We summarise these components in a parameter-free toolkit that we call Cluster Over-Time Stability Evaluation (CLOSE). In addition to that we present a fuzzy variant which we call FCSETS (Fuzzy Clustering Stability Evaluation of Time Series). These toolkits enable a number of advanced applications. One of these is parameter selection for any type of clustering algorithm. We demonstrate parameter selection as an example and evaluate results of classical clustering algorithms against a well-known evolutionary clustering algorithm. We then introduce a method for outlier detection in time series data based on CLOSE. We demonstrate the practicality of our approaches on three real world data sets and one generated data set.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Fig.1Illustration of the most important definitions. Lines between objects of a time series represent the development of the sequence[29] …”

mentioning

confidence: 99%

Cluster-based stability evaluation in time series data sets

2022

Self Cite

View full text Add to dashboard Cite

show abstract

The Price Development of Selected Energetic Commodities

MATASOVÁ,

ROUSEK,

BESTA

2024

Ad Alta: 14/01

View full text Add to dashboard Cite

The aim of the paper was to evaluate the price development of electrical energy and inflation as a commodity in the course of the past 10 years. The production of electrical energy decreased from 83.7 TWh in 2010 to 70.6 TWh in 2020 in the Czech Republic in accordance with the results achieved by using the methods of descriptive statistics, time series, correlation analysis and regression analysis. The consumption of energy slightly increased. The proportion of production to consumption declined and consumption was higher than production in 2020. The prices of electricity reveal oscillations influenced by the conflict in the Ukraine. The use of the sources of energy changed, however, without specific trends. The dependence of prices on inflation is considerable and direct. Energy policy ought to deal with the dependence on import and support the domestic production of energy.

show abstract

Clustering of Time Series Regarding Their Over-Time Stability

Cited by 2 publications

References 8 publications

Cluster-based stability evaluation in time series data sets

Cluster-based stability evaluation in time series data sets

The Price Development of Selected Energetic Commodities

Contact Info

Product

Resources

About