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ORCIDJosé Antonio Vilar
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2020 – today
- 2024
[j18]Ángel López-Oriona
, José Antonio Vilar:
Analyzing categorical time series with the package ctsfeatures. J. Comput. Sci. 76: 102233 (2024)- 2023
- [j17]Ángel López-Oriona, Christian H. Weiß, José Antonio Vilar:
Two novel distances for ordinal time series and their application to fuzzy clustering. Fuzzy Sets Syst. 468: 108590 (2023) - [j16]Ángel López-Oriona, José Antonio Vilar:
Machine learning for multivariate time series with the R package mlmts. Neurocomputing 537: 210-235 (2023) - [j15]Ángel López-Oriona, José Antonio Vilar, Pierpaolo D'Urso:
Hard and soft clustering of categorical time series based on two novel distances with an application to biological sequences. Inf. Sci. 624: 467-492 (2023) - [i5]Ángel López-Oriona, Christian Weiss, José Antonio Vilar:
Fuzzy clustering of ordinal time series based on two novel distances with economic applications. CoRR abs/2304.12249 (2023) - [i4]Ángel López-Oriona, José Antonio Vilar Fernández:
Ordinal time series analysis with the R package otsfeatures. CoRR abs/2304.12251 (2023) - [i3]Ángel López-Oriona, José Antonio Vilar Fernández:
Analyzing categorical time series with the R package ctsfeatures. CoRR abs/2304.12332 (2023) - [i2]Ángel López-Oriona, Pablo Montero-Manso, José Antonio Vilar Fernández:
Time series clustering based on prediction accuracy of global forecasting models. CoRR abs/2305.00473 (2023) - 2022
- [j14]Ángel López-Oriona, José Antonio Vilar, Pierpaolo D'Urso:
Quantile-based fuzzy clustering of multivariate time series in the frequency domain. Fuzzy Sets Syst. 443(Part): 115-154 (2022) - [j13]Ángel López-Oriona, Pierpaolo D'Urso, José Antonio Vilar, Borja R. Lafuente-Rego:
Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques. Int. J. Approx. Reason. 150: 55-82 (2022) - [j12]Ángel López-Oriona, José Antonio Vilar:
The bootstrap for testing the equality of two multivariate time series with an application to financial markets. Inf. Sci. 616: 255-275 (2022) - [j11]Ángel López-Oriona, Pierpaolo D'Urso, José Antonio Vilar, Borja R. Lafuente-Rego:
Spatial Weighted Robust Clustering of Multivariate Time Series Based on Quantile Dependence With an Application to Mobility During COVID-19 Pandemic. IEEE Trans. Fuzzy Syst. 30(9): 3990-4004 (2022) - [c4]Ángel López-Oriona, Pablo Montero-Manso, José Antonio Vilar:
Clustering of Time Series Based on Forecasting Performance of Global Models. AALTD@ECML/PKDD 2022: 18-33 - 2021
- [j10]Ángel López-Oriona, José Antonio Vilar:
Quantile cross-spectral density: A novel and effective tool for clustering multivariate time series. Expert Syst. Appl. 185: 115677 (2021) - [j9]Ángel López-Oriona, José Antonio Vilar:
Outlier detection for multivariate time series: A functional data approach. Knowl. Based Syst. 233: 107527 (2021) - [c3]Ángel López-Oriona, Pierpaolo D'Urso, José Antonio Vilar, Borja R. Lafuente-Rego:
Spatial weighted robust clustering of multivariate time series based on quantile dependence with an application to mobility during COVID-19 pandemic. WILF 2021 - [i1]Ángel López-Oriona, Pierpaolo D'Urso, José Antonio Vilar, Borja R. Lafuente-Rego:
Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques. CoRR abs/2109.11027 (2021)
2010 – 2019
- 2019
- [j8]Pablo Montero-Manso, Laura Morán-Fernández, Verónica Bolón-Canedo, José Antonio Vilar, Amparo Alonso-Betanzos:
Distributed classification based on distances between probability distributions in feature space. Inf. Sci. 496: 431-450 (2019) - [j7]Pablo Montero-Manso, José Antonio Vilar:
Two-sample homogeneity testing: A procedure based on comparing distributions of interpoint distances. Stat. Anal. Data Min. 12(3): 234-252 (2019) - 2018
- [j6]José Antonio Vilar, Borja R. Lafuente-Rego, Pierpaolo D'Urso:
Quantile autocovariances: A powerful tool for hard and soft partitional clustering of time series. Fuzzy Sets Syst. 340: 38-72 (2018) - 2016
- [j5]Borja R. Lafuente-Rego, José Antonio Vilar:
Clustering of time series using quantile autocovariances. Adv. Data Anal. Classif. 10(3): 391-415 (2016) - [e2]Ahlame Douzal Chouakria, José A. Vilar Fernández, Pierre-François Marteau:
Advanced Analysis and Learning on Temporal Data - First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers. Lecture Notes in Computer Science 9785, Springer 2016, ISBN 978-3-319-44411-6 [contents] - 2015
- [j4]Manuel García-Magariños, José Antonio Vilar:
A framework for dissimilarity-based partitioning clustering of categorical time series. Data Min. Knowl. Discov. 29(2): 466-502 (2015) - [c2]Borja R. Lafuente-Rego, José Antonio Vilar:
Fuzzy Clustering of Series Using Quantile Autocovariances. AALTD@PKDD/ECML 2015 - [c1]Borja R. Lafuente-Rego, José Antonio Vilar:
Fuzzy Clustering of Series Using Quantile Autocovariances. AALTD@PKDD/ECML (Revised Selected Papers) 2015: 49-64 - [e1]Ahlame Douzal Chouakria, José A. Vilar Fernández, Pierre-François Marteau, Elizabeth Ann Maharaj, Andrés M. Alonso Fernández, Edoardo Otranto, Maria-Irina Nicolae:
Proceedings of the 1st International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2015, co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, September 11, 2015. CEUR Workshop Proceedings 1425, CEUR-WS.org 2015 [contents] - 2010
- [j3]Sonia Pértega Díaz, José Antonio Vilar:
Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study. J. Classif. 27(3): 333-362 (2010) - [j2]José Antonio Vilar, A. M. Alonso, Juan Manuel Vilar:
Non-linear time series clustering based on non-parametric forecast densities. Comput. Stat. Data Anal. 54(11): 2850-2865 (2010)
2000 – 2009
- 2009
- [j1]Juan Manuel Vilar, José Antonio Vilar, Sonia Pértega Díaz:
Classifying Time Series Data: A Nonparametric Approach. J. Classif. 26(1): 3-28 (2009)
Coauthor Index
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