Polarization of forecast densities: a new approach to time series classification

DOI10.1016/j.csda.2013.10.008zbMath1471.62121OpenAlexW2050028138MaRDI QIDQ1615245

Elizabeth Ann Maharaj, Shen Liu, Brett A. Inder

Publication date: 2 November 2018

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

Full work available at URL: https://eprints.qut.edu.au/73236/3/73236a.pdf

zbMATH Keywords

time series classificationforecast densitiesbias-corrected bootstrappolarization measures

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

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Autoregressive prediction with rolling mechanism for time series forecasting with small sample size, Clustering nonlinear time series with neural network bootstrap forecast distributions, Wavelet-based clustering of sea level records

Cites Work

• Unnamed Item
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• Nonparametric estimation and inference about the overlap of two distributions
• Supervised classification for functional data: a weighted distance approach
• Phase and amplitude-based clustering for functional data
• Variational Bayes approach for model aggregation in unsupervised classification with Markovian dependency
• On bootstrapping two-stage least-squares estimates in stationary linear models
• Clustering financial time series: an application to mutual funds style analysis
• Adaptive clustering for time series: application for identifying cell cycle expressed genes
• Time series clustering based on forecast densities
• Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data
• Bootstrap prediction intervals for autoregressive time series
• Use of SVD-based probit transformation in clustering gene expression profiles
• Class prediction and gene selection for DNA microarrays using regularized sliced inverse regression
• Discrimination of locally stationary time series using wavelets
• Time series clustering and classification by the autoregressive metric
• Classification of gene functions using support vector machine for time-course gene expression data
• Assessing agreement of clustering methods with gene expression microarray data
• Time-frequency clustering and discriminant analysis.
• Clusters of time series
• Bispectral-based methods for clustering time series
• A fixed point characterization for bias of autoregressive estimators
• Comparing non-stationary and irregularly spaced time series
• A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples
• Toward an empirical analysis of polarization
• Non-linear time series clustering based on non-parametric forecast densities
• Bias-corrected bootstrap prediction intervals for autoregressive model: new alternatives with applications to tourism forecasting
• Bootstrap Prediction Intervals for Autoregression
• Distributional Overlap: Simple, Multivariate, Parametric, and Nonparametric Tests for Alienation, Convergence, and General Distributional Difference Issues
• The Bias of Autoregressive Coefficient Estimators
• Discrimination and Clustering for Multivariate Time Series
• Accounting for Lag Order Uncertainty in Autoregressions: the Endogenous Lag Order Bootstrap Algorithm
• On the Measurement of Polarization
• Bootstrap predictive inference for ARIMA processes
• Polarization: Concepts, Measurement, Estimation