Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Computational Statistics & Data Analysis
Bootstrap prediction intervals for autoregressive time series
Computational Statistics & Data Analysis
Use of SVD-based probit transformation in clustering gene expression profiles
Computational Statistics & Data Analysis
Discrimination of locally stationary time series using wavelets
Computational Statistics & Data Analysis
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
Classification of gene functions using support vector machine for time-course gene expression data
Computational Statistics & Data Analysis
Assessing agreement of clustering methods with gene expression microarray data
Computational Statistics & Data Analysis
Adaptive clustering for time series: Application for identifying cell cycle expressed genes
Computational Statistics & Data Analysis
Time series clustering based on forecast densities
Computational Statistics & Data Analysis
Non-linear time series clustering based on non-parametric forecast densities
Computational Statistics & Data Analysis
Supervised classification for functional data: A weighted distance approach
Computational Statistics & Data Analysis
Phase and amplitude-based clustering for functional data
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Comparing non-stationary and irregularly spaced time series
Computational Statistics & Data Analysis
A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples
Computational Statistics & Data Analysis
Bispectral-based methods for clustering time series
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data. However, if interest is in a specific future time period, methods that directly relate to forecasts of time series are much more appropriate. An approach to time series classification is proposed based on a polarization measure of forecast densities of time series. By fitting autoregressive models, forecast replicates of each time series are obtained via the bias-corrected bootstrap, and a stationarity correction is considered when necessary. Kernel estimators are then employed to approximate forecast densities, and discrepancies of forecast densities of pairs of time series are estimated by a polarization measure, which evaluates the extent to which two densities overlap. Following the distributional properties of the polarization measure, a discriminant rule and a clustering method are proposed to conduct the supervised and unsupervised classification, respectively. The proposed methodology is applied to both simulated and real data sets, and the results show desirable properties.