Quantile-based fuzzy \(C\)-means clustering of multivariate time series: robust techniques
From MaRDI portal
Publication:2092446
DOI10.1016/j.ijar.2022.07.010OpenAlexW3199817096MaRDI QIDQ2092446
Borja Lafuente-Rego, Ángel López-Oriona, Pierpaolo D'Urso, José Antonio Vilar
Publication date: 2 November 2022
Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2109.11027
trimmingmultivariate time seriesnoise clusterexponential distancequantile cross-spectral densityrobust fuzzy \(C\)-means
Related Items (3)
A three-way clustering method based on improved density peaks algorithm and boundary detection graph ⋮ OWA-based robust fuzzy clustering of time series with typicality degrees ⋮ The bootstrap for testing the equality of two multivariate time series with an application to financial markets
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Wavelet-based fuzzy clustering of time series
- Quantile spectral processes: asymptotic analysis and inference
- Wavelets-based clustering of multivariate time series
- Noise fuzzy clustering of time series by autoregressive metric
- Fuzzy clustering of time series in the frequency domain
- A periodogram-based metric for time series classification
- Impulse response analysis in nonlinear multivariate models
- Robust fuzzy clustering of multivariate time trajectories
- Quantile autocovariances: a powerful tool for hard and soft partitional clustering of time series
- Alternative c-means clustering algorithms
- GARCH-based robust clustering of time series
- Hierarchical time series clustering on tail dependence with linkage based on a multivariate copula approach
- Model-based fuzzy time series clustering of conditional higher moments
- Robust fuzzy clustering of time series based on B-splines
- Clustering nonlinear time series with neural network bootstrap forecast distributions
- Clustering time series by linear dependency
- Of copulas, quantiles, ranks and spectra: an \(L_{1}\)-approach to spectral analysis
- Clustering of time series using quantile autocovariances
- Outliers in multivariate time series
- Dynamic Models for Volatility and Heavy Tails
- A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS
- Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
- Discrimination and Clustering for Multivariate Time Series
- A significance test for classifying arma models
- Quantile coherency: A general measure for dependence between cyclical economic variables
- A Framework for Feature Selection in Clustering
- Time Series Clustering and Classification
- Outlier Detection in Multivariate Time Series by Projection Pursuit
- A simulation study of artificial neural networks for nonlinear time-series forecasting
This page was built for publication: Quantile-based fuzzy \(C\)-means clustering of multivariate time series: robust techniques
