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.

提出了一种新的非线性时间序列数据聚类方法。它基于预测分布，通过使用前馈神经网络和配对引导程序估计。该过程显示为纯自回归相关结构提供一致的结果。它在非线性自回归过程的一般类别中是无模型的，并且它避免了数据生成过程的有限维模型规范。报告了蒙特卡罗研究的结果，以研究所提出的时间序列聚类方法的有限样本性能。还讨论了对经济时间序列真实数据集的应用。