5 citations · 9 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…