Characterization of time series for analyzing of the evolution of time series clusters

Serra, Ana P.; Zárate, Luis Enrique

doi:10.1016/j.eswa.2014.08.012

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…The strategies to define the amplitude of the temporal windows vary according to the study domain and the domain concepts' frequency variation over time. Since the aim is to search for cut-off points in time where there are changes of interest for the analysis, having information about the study field's evolution can indicate the window's amplitude (Serra and Z arate, 2015).…”

Section: Definition Of the Period To Identify The Evolution Of Scient...mentioning

confidence: 99%

SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

Zárate

Rodrigues

Dias³

et al. 2023

LHT

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PurposeThe scientific community shares a heritage of knowledge generated by several different fields of research. Identifying how scientific interest evolves is relevant for recording and understanding research trends and society’s demands.Design/methodology/approachThis article presents SciBR-M, a novel method to identify scientific interest evolution from bibliographic material based on Formal Concept Analysis. The SciBR-M aims to describe the thematic evolution surrounding a field of research. The method begins by hierarchically organising sub-domains within the field of study to identify the themes that are more relevant. After this organisation, we apply a temporal analysis that extracts implication rules with minimal premises and a single conclusion, which are helpful to observe the evolution of scientific interest in a specific field of study. To analyse the results, we consider support, confidence, and lift metrics to evaluate the extracted implications.FindingsThe authors applied the SciBR-M method for the Educational Data Mining (EDM) field considering 23 years since the first publications. In the digital libraries context, SciBR-M allows the integration of the academy, education, and cultural memory, in relation to a study domain.Social implicationsCultural changes lead to the production of new knowledge and to the evolution of scientific interest. This knowledge is part of the scientific heritage of society and should be transmitted in a structured and organised form to future generations of scientists and the general public.Originality/valueThe method, based on Formal Concept Analysis, identifies the evolution of scientific interest to a field of study. SciBR-M hierarchically organises bibliographic material to different time periods and explores this hierarchy from proper implication rules. These rules permit identifying recurring themes, i.e. themes subset that received more attention from the scientific community during a specific period. Analysing these rules, it is possible to identify the temporal evolution of scientific interest in the field of study. This evolution is observed by the emergence, increase or decrease of interest in topics in the domain. The SciBR-M method can be used to register and analyse the scientific, cultural heritage of a field of study. In addition, the authors can use the method to stimulate the process of creating knowledge and innovation and encouraging the emergence of new research.

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Section: Definition Of the Period To Identify The Evolution Of Scient...mentioning

confidence: 99%

SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

Zárate

Rodrigues

Dias³

et al. 2023

LHT

View full text Add to dashboard Cite

show abstract

“…Assim, o conhecimento acerca do sistema onde as STs são coletadas, nos permite conjecturar a periodicidade das variações, e consequentemente, a amplitude ideal do janelamento. Como critério de escolha da amplitude da janela w h , devem ser observadas as séries que possuem menor variabilidade no tempo [Serra and Zárate 2015]. Para este trabalho consideramos janelas com tamanho fixo de 6 pontos discretos.…”

Section: Janelamento E Caracterização De Séries Temporaisunclassified

Extreme Events Characterization on Time Series

Rodrigues¹,

Zárate²

2020

Anais Do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020)

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The use of sensors in environments where they require constant monitoring has been increasing in recent years. The main goal is to guarantee the effectiveness, safety, and smooth functioning of the system. To identify the occurrence of abnormal events, we propose a methodology that aims to detect patterns that can lead to abrupt changes in the behavior of the sensor signals. To achieve this objective, we provide a strategy to characterize the time series, and we use a clustering technique to analyze the temporal evolution of the sensor system. To validate our methodology, we propose the clusters’ stability index by windowing. Also, we have developed a parameterizable time series generator, which allows us to represent different operational scenarios for a sensor system where extreme anomalies may arise.

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“…The Genetic Algorithm considered in this paper can be included in the area of time series segmentation [7,24,25,26,9,10,11,17,13]. Our main objective is to devise an unsupervised methodology to identify time segments with similar statistical behaviour [23].…”

Section: Summary Of the Algorithmmentioning

confidence: 99%

“…Time series segmentation is a research field, aiming to provide a compact representation of the time series values, dividing it into segments and using an abstract representation of each segment. It is very important for time series representation and time series mining [6,7,8] and is commonly used as a pre-processing step for different mining tasks [8,9,10,11] (e.g. clustering, classification or motif detection) and for data compressing [12,13].…”

Section: Introductionmentioning

confidence: 99%

On the use of evolutionary time series analysis for segmenting paleoclimate data

et al. 2019

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Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series.

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Characterization of time series for analyzing of the evolution of time series clusters

Cited by 8 publications

References 20 publications

SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

Extreme Events Characterization on Time Series

On the use of evolutionary time series analysis for segmenting paleoclimate data

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