Asymptotic theory of statistical inference
Asymptotic theory of statistical inference
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Forecasting nonlinear time series with neural network sieve bootstrap
Computational Statistics & Data Analysis
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
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
Time series clustering based on forecast densities
Computational Statistics & Data Analysis
A periodogram-based metric for time series classification
Computational Statistics & Data Analysis
Clustering of time series data-a survey
Pattern Recognition
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
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The problem of clustering time series is studied for a general class of non-parametric autoregressive models. The dissimilarity between two time series is based on comparing their full forecast densities at a given horizon. In particular, two functional distances are considered: L^1 and L^2. As the forecast densities are unknown, they are approximated using a bootstrap procedure that mimics the underlying generating processes without assuming any parametric model for the true autoregressive structure of the series. The estimated forecast densities are then used to construct the dissimilarity matrix and hence to perform clustering. Asymptotic properties of the proposed method are provided and an extensive simulation study is carried out. The results show the good behavior of the procedure for a wide variety of nonlinear autoregressive models and its robustness to non-Gaussian innovations. Finally, the proposed methodology is applied to a real dataset involving economic time series.