5 citations · 9 across the 11 of their papers we have counts for
11 papers
Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series
Ziling Ma, Junshu Jiang, Ángel López-Oriona +2
We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series. The central idea is to learn an adaptive…
Amortized Neural Clustering of Time Series based on Statistical Features
Ángel López-Oriona, Ying Sun
This paper introduces an algorithm-agnostic approach to feature-based time series clustering via amortized neural inference. By training neural networks to approximate the optimal…
FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis
Ziling Ma, Ángel López-Oriona, Hernando Ombao +1
Clustering multivariate time series data is a crucial task in many domains, as it enables the identification of meaningful patterns and groups in time-evolving data. Traditional ap…
Dependence-based fuzzy clustering of functional time series
Angel Lopez-Oriona, Ying Sun, Han Lin Shang
Time series clustering is essential in scientific applications, yet methods for functional time series, collections of infinite-dimensional curves treated as random elements in a H…
Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data
Ángel López-Oriona, Ying Sun, Rosa M. Crujeiras
Time series clustering is an essential machine learning task with applications in many disciplines. While the majority of the methods focus on time series taking values on the real…
Time series clustering based on prediction accuracy of global forecasting models
Ángel López Oriona, Pablo Montero Manso, José Antonio Vilar Fernández
In this paper, a novel method to perform model-based clustering of time series is proposed. The procedure relies on two iterative steps: (i) K global forecasting models are fitted…
Analyzing categorical time series with the R package ctsfeatures
Ángel López Oriona, José Antonio Vilar Fernández
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.…
Ordinal time series analysis with the R package otsfeatures
Ángel López Oriona, José Antonio Vilar Fernández
The 21st century has witnessed a growing interest in the analysis of time series data. Whereas most of the literature on the topic deals with real-valued time series, ordinal time…
Fuzzy clustering of ordinal time series based on two novel distances with economic applications
Ángel López Oriona, Christian Weiss, José Antonio Vilar
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 consi…
Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques
Ángel López-Oriona, Pierpaolo D'Urso, José Antonio Vilar +1
Three robust methods for clustering multivariate time series from the point of view of generating processes are proposed. The procedures are robust versions of a fuzzy C-means mode…
Quantile-based fuzzy clustering of multivariate time series in the frequency domain
Ángel López-Oriona, José A. Vilar, Pierpaolo-D'Urso
A novel procedure to perform fuzzy clustering of multivariate time series generated from different dependence models is proposed. Different amounts of dissimilarity between the gen…