Time series clustering based on forecast densities

DOI10.1016/J.CSDA.2006.04.035zbMath1157.62484OpenAlexW2081125316MaRDI QIDQ1010412

J. Martínez

Publication date: 6 April 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.04.035

zbMATH Keywords

predictionbootstrapcluster analysisKyoto protocolnonparametric density estimation\(L^{2}\)-distance

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

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Uses Software

• TRAMO

Cites Work

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• Clustering financial time series: an application to mutual funds style analysis
• Asymptotics for linear processes
• A new mixing notion and functional central limit theorems for a sieve bootstrap in time series
• On sieve bootstrap prediction intervals.
• A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS
• Bootstrap Prediction Intervals for Autoregression
• REGRESSION, AUTOREGRESSION MODELS
• Regression and time series model selection in small samples
• Discrimination and Clustering for Multivariate Time Series
• Estimation, Prediction, and Interpolation for Nonstationary Series with the Kalman Filter
• A significance test for classifying arma models
• Saving computer time in constructing consistent bootstrap prediction intervals for autoregressive processes
• Bootstrap predictive inference for ARIMA processes
• Bayesian analysis of switching ARCH models
• Forecasting time series with sieve bootstrap

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