La Rocca, Michele; Giordano, Francesco; Perna, Cira Clustering nonlinear time series with neural network bootstrap forecast distributions. (English) Zbl 1520.62109 Int. J. Approx. Reasoning 137, 1-15 (2021). Summary: A new method for clustering nonlinear time series data is proposed. It is based on the forecast distributions, which are estimated by using a feed-forward neural network and the pair bootstrap. The procedure is shown to deliver consistent results for pure autoregressive dependent structures. It is model-free within a general class of nonlinear autoregression processes, and it avoids the specification of a finite dimensional model for the data generating process. The results of a Monte Carlo study are reported in order to investigate the finite sample performances of the proposed time series clustering approach. An application to a real dataset of economic time series is also discussed. Cited in 2 Documents MSC: 62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH) 62H30 Classification and discrimination; cluster analysis (statistical aspects) 68T05 Learning and adaptive systems in artificial intelligence 91B84 Economic time series analysis Keywords:cluster analysis; feedforward neural networks; bootstrap; time series; economic data PDFBibTeX XMLCite \textit{M. La Rocca} et al., Int. J. Approx. Reasoning 137, 1--15 (2021; Zbl 1520.62109) Full Text: DOI References: [1] Iorio, C.; Frasso, G.; D’Ambrosio, A.; Siciliano, R., A P-spline based clustering approach for portfolio selection, Expert Syst. Appl., 95, 88-103 (2018) [2] Gullo, F.; Ponti, G.; Tagarelli, A.; Tradigo, G.; Veltri, P., A time series approach for clustering mass spectrometry data, J. Comput. Sci., 3, 344-355 (2012) [3] D’Urso, P.; De Giovanni, L.; Massari, R., Time series clustering by a robust autoregressive metric with application to air pollution, Chemom. Intell. Lab. 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