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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >ROBUST SIEVE BOOTSTRAP PREDICTION INTERVALS FOR CONTAMINATED TIME SERIES
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ROBUST SIEVE BOOTSTRAP PREDICTION INTERVALS FOR CONTAMINATED TIME SERIES

机译：污染时间序列的鲁棒筛分自举预测间隔

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摘要

Time series prediction is of primary importance in a variety of applications from several science fields, like engineering, finance, earth sciences, etc. Time series prediction can be divided in to two main tasks, point and interval estimation. Estimating prediction intervals, is in some cases more important than point estimation mainly because it indicates the likely uncertainty in the prediction process. Recently, the sieve bootstrap method has been successfully used in prediction of nonlinear time series. In this work, we study the performance of the prediction intervals based on the sieve bootstrap technique, which does not require the distributional assumption of normality as most techniques that are found in the literature. The construction of prediction intervals in the presence of different types of outliers is not robust from a distributional point of view, leading to an undesirable increase in the length of the prediction intervals. In the analysis of time series, it is common to have irregular observations that have different types of outliers. For this reason, we propose the construction of prediction intervals for returns based on the winsorized residual and bootstrap techniques for time series prediction. We propose a novel, simple and distribution free resampling technique for developing robust prediction intervals for returns and volatilities for GARCH models. The proposed procedure is illustrated by an application to known synthetic and real-time series.
机译：时间序列预测在工程，金融，地球科学等多个科学领域的各种应用中具有最重要的意义。时间序列预测可以分为两个主要任务，即点估计和区间估计。在某些情况下，估计预测间隔比点估计更重要，主要是因为它表明了预测过程中可能存在的不确定性。近来，筛分引导法已成功地用于非线性时间序列的预测。在这项工作中，我们研究了基于筛网自举技术的预测区间的性能，该技术不需要像文献中发现的大多数技术一样正态分布假设。从分布的角度来看，在存在不同类型的异常值时，预测间隔的构造不稳健，从而导致预测间隔长度的不希望有的增加。在时间序列分析中，通常会出现具有不同类型异常值的不规则观测。由于这个原因，我们提出基于时间序列预测的Winsorized残差和自举技术构建收益率预测间隔的方法。我们提出了一种新颖，简单且无分布的重采样技术，用于为GARCH模型的收益率和波动率开发鲁棒的预测区间。通过对已知的合成和实时序列的应用来说明所提出的过程。

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