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
Multivariate time series modeling and classification via hierarchical VAR mixtures
Computational Statistics & Data Analysis
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
Clustering heteroskedastic time series by model-based procedures
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
Comparison of time series using subsampling
Computational Statistics & Data Analysis
A periodogram-based metric for time series classification
Computational Statistics & Data Analysis
Autocorrelation-based fuzzy clustering of time series
Fuzzy Sets and Systems
Clustering of time series data-a survey
Pattern Recognition
Non-linear time series clustering based on non-parametric forecast densities
Computational Statistics & Data Analysis
Polarization of forecast densities: A new approach to time series classification
Computational Statistics & Data Analysis
A random-projection based test of Gaussianity for stationary processes
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
A new test of hypothesis for classifying stationary time series based on the bias-adjusted estimators of the fitted autoregressive model is proposed. It is shown theoretically that the proposed test has desirable properties. Simulation results show that when time series are short, the size and power estimates of the proposed test are reasonably good, and thus this test is reliable in discriminating between short-length time series. As the length of the time series increases, the performance of the proposed test improves, but the benefit of bias-adjustment reduces. The proposed hypothesis test is applied to two real data sets: the annual real GDP per capita of six European countries, and quarterly real GDP per capita of five European countries. The application results demonstrate that the proposed test displays reasonably good performance in classifying relatively short time series.