Listar Modelización, Optimización e Inferencia Estadística (MODES) por autor "López-Oriona, Ángel"

Mostrando ítems 1-14 de 14

    • Analyzing categorical time series with the R package ctsfeatures 

      López-Oriona, Ángel; Vilar, José (Elsevier B.V., 2024-03)
      [Absctract]: Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with real-valued time series, categorical time series have received much less attention. However, the development of ...

    • F4: An All-Purpose Tool for Multivariate Time Series Classification 

      López-Oriona, Ángel; Vilar, José (MDPI, 2021)
      [Abstract] We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been ...

    • Machine learning for multivariate time series with the R package mlmts 

      López-Oriona, Ángel; Vilar, José (Elsevier B.V., 2023-06)
      [Abstract]: Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with univariate time series, multivariate time series have typically received much less attention. However, the ...

    • Modeling the Number of People Infected With SARS-COV-2 From Wastewater Viral Load in Northwest Spain 

      Vallejo, J. A.; Trigo Tasende, Noelia; Rumbo-Feal, Soraya; Conde-Pérez, Kelly; López-Oriona, Ángel; Barbeito, Inés; Vaamonde, Manuel; Tarrío-Saavedra, Javier; Reif López, Rubén; Ladra, Susana; Rodiño-Janeiro, Bruno Kotska; Nasser-Ali, Mohammed; Cid, Ángeles; Veiga, María Carmen; Acevedo, Antón; Lamora, Carlos; Bou, Germán; Cao, Ricardo; Poza, Margarita (Elsevier, 2022)
      [Abstract] The quantification of the SARS-CoV-2 RNA load in wastewater has emerged as a useful tool to monitor COVID–19 outbreaks in the community. This approach was implemented in the metropolitan area of A Coruña (NW ...

    • Ordinal Time Series Analysis with the R Package otsfeatures 

      López-Oriona, Ángel; Vilar, José (MDPI, 2023-06)
      [Abstract] The 21st century has witnessed a growing interest in the analysis of time series data. While most of the literature on the topic deals with real-valued time series, ordinal time series have typically received ...

    • Outlier Detection for Multivariate Time Series: A Functional Data Approach ® 

      López-Oriona, Ángel; Vilar, José (Elsevier, 2021)
      [Abstract] A method for detecting outlier samples in a multivariate time series dataset is proposed. It is assumed that an outlying series is characterized by having been generated from a different process than those ...

    • Quantile Cross-Spectral Density: A Novel and Effective Tool for Clustering Multivariate Time Series 

      López-Oriona, Ángel; Vilar, José (Elsevier, 2021)
      [Abstract] Clustering of multivariate time series is a central problem in data mining with applications in many fields. Frequently, the clustering target is to identify groups of series generated by the same multivariate ...

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

      López-Oriona, Ángel; D'Urso, Pierpaolo; Vilar, José; Lafuente Rego, Borja Raúl (Elsevier, 2022-11)
      [Abstract]: Robust fuzzy clustering of multivariate time series is addressed when the clustering purpose is grouping together series generated from similar stochastic processes. Robustness to the presence of anomalous ...

    • Quantile-Based Fuzzy Clustering of Multivariate Time Series in the Frequency Domain 

      López-Oriona, Ángel; Vilar, José; D'Urso, Pierpaolo (Elsevier, 2022)
      [Abstract] A novel procedure to perform fuzzy clustering of multivariate time series generated from different dependence models is proposed. Different amounts of dissimilarity between the generating models or changes on ...

    • Robust Methods for Soft Clustering of Multidimensional Time Series 

      López-Oriona, Ángel; D'Urso, Pierpaolo; Vilar, José; Lafuente Rego, Borja Raúl (MDPI, 2021)
      [Abstract] Three robust algorithms for clustering multidimensional time series from the perspective of underlying processes are proposed. The methods are robust extensions of a fuzzy C-means model based on estimates of the ...

    • The Bootstrap for Testing the Equality of Two Multivariate Stochastic Processes with an Application to Financial Markets 

      López-Oriona, Ángel; Vilar, José (MDPI, 2022)
      [Abstract] The problem of testing the equality of generating processes of two multivariate time series is addressed in this work. To this end, we construct two tests based on a distance measure between stochastic processes. ...

    • Two novel distances for ordinal time series and their application to fuzzy clustering 

      López-Oriona, Ángel; Weiß, Christian H.; Vilar, José (Elsevier B.V., 2023-09-30)
      [Abstract]: Time series clustering is a central machine learning task with applications in many fields. While the majority of the methods focus on real-valued time series, very few works consider series with discrete ...

    • Unsupervised classification of categorical time series through innovative distances 

      López-Oriona, Ángel; Vilar, José; D'Urso, Pierpaolo (Avestia Publishing, 2022)
      [Abstract]: In this paper, two novel distances for nominal time series are introduced. Both of them are based on features describing the serial dependence patterns between each pair of categories. The first dissimilarity ...

    • Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain 

      Trigo-Tasende, Noelia; Vallejo, Juan Andrés; Rumbo-Feal, Soraya; Conde-Pérez, Kelly; Vaamonde, Manuel; López-Oriona, Ángel; Barbeito, Inés; Nasser-Ali, Mohammed; Reif López, Rubén; Rodiño-Janeiro, Bruno Kotska; Fernández-Álvarez, Elisa; Iglesias Corrás, Iago; Tarrío-Saavedra, Javier; Tomás, Laura; Gallego-García, Pilar; Posada, David; Bou, Germán; López-de-Ullibarri, Ignacio; Cao, Ricardo; Susana, Ladra; Poza, Margarita (Springer, 2023-07)
      [Abstract]: Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running ...