José Antonio Vilar

Christian H. Weiß

Fuzzy Sets Syst., September, 2023

Machine learning for multivariate time series with the R package <i>mlmts</i>.

[BibT_eX]

[DOI]

Neurocomputing, June, 2023

Hard and soft clustering of categorical time series based on two novel distances with an application to biological sequences.

[BibT_eX]

[DOI]

Inf. Sci., May, 2023

Time series clustering based on prediction accuracy of global forecasting models.

[BibT_eX]

[DOI]

José Antonio Vilar Fernández

CoRR, 2023

Analyzing categorical time series with the R package ctsfeatures.

[BibT_eX]

[DOI]

José Antonio Vilar Fernández

CoRR, 2023

Ordinal time series analysis with the R package otsfeatures.

[BibT_eX]

[DOI]

José Antonio Vilar Fernández

CoRR, 2023

Fuzzy clustering of ordinal time series based on two novel distances with economic applications.

[BibT_eX]

[DOI]

Christian Weiss

CoRR, 2023

2022

Spatial Weighted Robust Clustering of Multivariate Time Series Based on Quantile Dependence With an Application to Mobility During COVID-19 Pandemic.

[BibT_eX]

[DOI]

IEEE Trans. Fuzzy Syst., 2022

The bootstrap for testing the equality of two multivariate time series with an application to financial markets.

[BibT_eX]

[DOI]

Inf. Sci., 2022

Quantile-based fuzzy <i>C</i>-means clustering of multivariate time series: Robust techniques.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2022

Quantile-based fuzzy clustering of multivariate time series in the frequency domain.

[BibT_eX]

[DOI]

Fuzzy Sets Syst., 2022

Clustering of Time Series Based on Forecasting Performance of Global Models.

[BibT_eX]

[DOI]

Proceedings of the Advanced Analytics and Learning on Temporal Data, 2022

2021

Outlier detection for multivariate time series: A functional data approach.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2021

Quantile cross-spectral density: A novel and effective tool for clustering multivariate time series.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2021

Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques.

[BibT_eX]

[DOI]

CoRR, 2021

2019

Two-sample homogeneity testing: A procedure based on comparing distributions of interpoint distances.

[BibT_eX]

[DOI]

Stat. Anal. Data Min., 2019

Distributed classification based on distances between probability distributions in feature space.

[BibT_eX]

[DOI]

Laura Morán-Fernández

Verónica Bolón-Canedo

Amparo Alonso-Betanzos

Inf. Sci., 2019

2018

Quantile autocovariances: A powerful tool for hard and soft partitional clustering of time series.

[BibT_eX]

[DOI]

Fuzzy Sets Syst., 2018

2016

Clustering of time series using quantile autocovariances.

[BibT_eX]

[DOI]

Adv. Data Anal. Classif., 2016

2015

A framework for dissimilarity-based partitioning clustering of categorical time series.

[BibT_eX]

[DOI]

Manuel García-Magariños

Data Min. Knowl. Discov., 2015

Fuzzy Clustering of Series Using Quantile Autocovariances.

[BibT_eX]

[DOI]

Proceedings of the 1st International Workshop on Advanced Analytics and Learning on Temporal Data, 2015

2010

Non-linear time series clustering based on non-parametric forecast densities.

[BibT_eX]

[DOI]

A. M. Alonso

Juan Manuel Vilar

Comput. Stat. Data Anal., 2010

Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study.

[BibT_eX]

[DOI]

Sonia Pértega Díaz

J. Classif., 2010

2009

Classifying Time Series Data: A Nonparametric Approach.

[BibT_eX]

[DOI]

Juan Manuel Vilar