Results 11 to 20 of about 303,598 (207)
A benchmark study on time series clustering
Machine Learning with Applications, 2020 This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive — the state of the art repository of time series data.
Ali Javed, Byung Suk Lee, Donna M. Rizzo
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A review of subsequence time series clustering. [PDF]
ScientificWorldJournal, 2014 Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and ...
Zolhavarieh S, Aghabozorgi S, Teh YW.
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Time series clustering of COVID-19 pandemic-related data [PDF]
Data Science and Management, 2023 The COVID-19 pandemic continues to impact daily life worldwide. It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic.
Luo Z, Zhang L, Liu N, Wu Y.
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Coresets for Time Series Clustering [PDF]
SSRN Electronic Journal, 2021 We study the problem of constructing coresets for clustering problems with time series data. This problem has gained importance across many fields including biology, medicine, and economics due to the proliferation of sensors facilitating real-time measurement and rapid drop in storage costs. In particular, we consider the setting where the time series
Lingxiao Huang +2 more
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Measuring Extremal Clustering in Time Series
Engineering Proceedings, 2023 The propensity of data to cluster at extreme values is important for risk assessment. For example, heavy rain over time leads to catastrophic floods. The extremal index is a measure of Extreme Values Theory that allows measurement of the degree of high ...
Marta Ferreira
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Lag penalized weighted correlation for time series clustering. [PDF]
BMC Bioinformatics, 2020 Background The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data.
Chandereng T, Gitter A.
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Clustering discrete-valued time series [PDF]
Advances in Data Analysis and Classification, 2020 There is a need for the development of models that are able to account for discreteness in data, along with its time series properties and correlation. Our focus falls on INteger-valued AutoRegressive (INAR) type models. The INAR type models can be used in conjunction with existing model-based clustering techniques to cluster discrete-valued time ...
Tyler Roick +2 more
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Clustering time series based on dependence structure. [PDF]
PLoS ONE, 2018 The clustering of time series has attracted growing research interest in recent years. The most popular clustering methods assume that the time series are only linearly dependent but this assumption usually fails in practice. To overcome this limitation,
Beibei Zhang, Baiguo An
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Satellite Image Time Series Clustering via Time Adaptive Optimal Transport
Remote Sensing, 2021 Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised ...
Zheng Zhang +3 more
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CLUSTERING MACROECONOMIC TIME SERIES [PDF]
ECONOMETRICS, 2018 14 pages, 3 figures, 1 ...
Augustyński, Iwo +1 more
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