The statistical theory of linear systems
The statistical theory of linear systems
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Discrimination of locally stationary time series using wavelets
Computational Statistics & Data Analysis
A time series bootstrap procedure for interpolation intervals
Computational Statistics & Data Analysis
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
Clustering of discretely observed diffusion processes
Computational Statistics & Data Analysis
Autocorrelation-based fuzzy clustering of time series
Fuzzy Sets and Systems
Non-linear time series clustering based on non-parametric forecast densities
Computational Statistics & Data Analysis
A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples
Computational Statistics & Data Analysis
Polarization of forecast densities: A new approach to time series classification
Computational Statistics & Data Analysis
Unsupervised learning algorithm for time series using bivariate AR(1) model
Expert Systems with Applications: An International Journal
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A new clustering method for time series is proposed, based on the full probability density of the forecasts. First, a resampling method combined with a nonparametric kernel estimator provides estimates of the forecast densities. A measure of discrepancy is then defined between these estimates and the resulting dissimilarity matrix is used to carry out the required cluster analysis. Applications of this method to both simulated and real life data sets are discussed.