Search Results - subject:"Time series clustering"

A Conditional Value-at-Risk Based Portfolio Selection With Dynamic Tail Dependence Clustering

De Luca, Giovanni; Zuccolotto, Paola - Volkswirtschaftliche Fakultät, … - 2013

In this paper we propose a portfolio selection procedure specifically designed to protect investments during financial crisis periods. To this aim, we focus attention on the lower tails of the returns distributions and use a combination of statistical tools able to take into account the joint...

Persistent link: https://www.econbiz.de/10011111260

Regional labor dynamics and their relevance in the national aggregate : a time series clustering application for Chile

Chávez Bustamante, Felipe O. G.; Mondaca-Marino, Cristian - In: Estudios de economía aplicada : revista promovida por … 36 (2018) 3, pp. 961-978

Persistent link: https://www.econbiz.de/10011975866

A double clustering algorithm for financial time series based on extreme events

De Luca, Giovanni; Zuccolotto, Paola - In: Statistics & Risk Modeling 34 (2017) 1-2, pp. 1-12

Abstract This paper is concerned with a procedure for financial time series clustering, aimed at creating groups of …

Persistent link: https://www.econbiz.de/10014621246

Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators

Aielli, Gian Piero; Caporin, Massimiliano - Dipartimento di Scienze Economiche "Marco Fanno", … - 2011

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a cluster- ing structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to...

Persistent link: https://www.econbiz.de/10009025296

Clustering Finger Motion Data from Virtual Reality-Based Training to Analyze Patients with Mild Cognitive Impairment

Martono, Niken Prasasti; Yamaguchi, Takehiko; Maeta, Takuya - In: International Journal of Software Science and … 8 (2016) 4, pp. 29-42

individuals with MCI in order to characterize groups of participants. The authors implemented a time series clustering algorithm …

Persistent link: https://www.econbiz.de/10012048032

Variance clustering improved dynamic conditional correlation MGARCH estimators

Aielli, Gian Piero; Caporin, Massimiliano - In: Computational Statistics & Data Analysis 76 (2014) C, pp. 556-576

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact the Dynamic Conditional Correlation (DCC) model is extended by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to...

Persistent link: https://www.econbiz.de/10010871422

Fast clustering of GARCH processes via Gaussian mixture models

Aielli, Gian Piero; Caporin, Massimiliano - In: Mathematics and Computers in Simulation (MATCOM) 94 (2013) C, pp. 205-222

The financial econometrics literature includes several Multivariate GARCH models where the model parameter matrices depend on a clustering of financial assets. Those classes might be defined a priori or data-driven. When the latter approach is followed, one method for deriving asset groups is...

Persistent link: https://www.econbiz.de/10010751789

Model-based clustering of time series in group-specific functional subspaces

Bouveyron, Charles; Jacques, Julien - In: Advances in Data Analysis and Classification 5 (2011) 4, pp. 281-300

Persistent link: https://www.econbiz.de/10009404126

Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study

Díaz, Sonia; Vilar, José - In: Journal of Classification 27 (2010) 3, pp. 333-362

Persistent link: https://www.econbiz.de/10008775695

A TIME SERIES KNOWLEDGE MINING FRAMEWORK EXPLOITING THE SYNERGY BETWEEN SUBSEQUENCE CLUSTERING AND PREDICTIVE MARKOVIAN MODELS

Georgescu, V. - In: Fuzzy Economic Review XIV (2009) 1, pp. 41-66

series clustering and predictive tools such as Hidden Markov Models. Many tasks for temporal data mining rely heavily on the … taxonomy for numeric and symbolic time series and comprehensive categorization of distance measures. Subsequence time series …This paper proposes a time series knowledge mining framework, designed to favor the synergy between subsequence time …

Persistent link: https://www.econbiz.de/10008568507