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Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data
Time series clustering is an essential machine learning task with applications in many disciplines.
New bootstrap tests for categorical time series. A comparative study
To this aim, we propose three tests relying on a dissimilarity measure between categorical processes.
Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques
Robustness to the presence of outliers is achieved by using the so-called metric, noise and trimmed approaches.
Quantile-based fuzzy clustering of multivariate time series in the frequency domain
Different amounts of dissimilarity between the generating models or changes on the dynamic behaviours over time are some arguments justifying a fuzzy approach, where each series is associated to all the clusters with specific membership levels.
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