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.

Clustering nonlinear time series with neural network bootstrap forecast distributions

La Rocca M.
;
Giordano F.;Perna C.
2021

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4768187
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