Time series: theory and methods
Time series: theory and methods
Using cluster analysis to classify time series
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Comparison of non-stationary time series in the frequency domain
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
Clustering of biological time series by cepstral coefficients based distances
Pattern Recognition
Clustering heteroskedastic time series by model-based procedures
Computational Statistics & Data Analysis
Adaptive clustering for time series: Application for identifying cell cycle expressed genes
Computational Statistics & Data Analysis
Which Distance for the Identification and the Differentiation of Cell-Cycle Expressed Genes?
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Clustering of discretely observed diffusion processes
Computational Statistics & Data Analysis
Fault detection in multivariate signals with applications to gas turbines
IEEE Transactions on Signal Processing
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
Pattern Recognition Letters
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
A data-driven test to compare two or multiple time series
Computational Statistics & Data Analysis
Classification trees for time series
Pattern Recognition
Wavelets-based clustering of multivariate time series
Fuzzy Sets and Systems
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
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The statistical discrimination and clustering literature has studied the problem of identifying similarities in time series data. Some studies use non-parametric approaches for splitting a set of time series into clusters by looking at their Euclidean distances in the space of points. A new measure of distance between time series based on the normalized periodogram is proposed. Simulation results comparing this measure with others parametric and non-parametric metrics are provided. In particular, the classification of time series as stationary or as non-stationary is discussed. The use of both hierarchical and non-hierarchical clustering algorithms is considered. An illustrative example with economic time series data is also presented.