观测驱动指数平滑法

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Stat Pub Date : 2023-12-07 DOI:10.1002/sta4.642

Dimitris Karlis, Xanthi Pedeli, Cristiano Varin

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引用次数: 0

摘要

本文介绍了一种利用观测驱动模型建立的指数平滑法预测计数时间序列的方法。所提出的方法易于实施，解释起来也很简单。本文还提出了一种方法的变体，以处理异常值对预测的影响。通过模拟研究了该方法的性能，并通过分析 2008-2021 年在意大利观察到的登革热月病例数进行了说明。为使读者能够重现文章中讨论的结果，提供了一个 R 软件包。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Observation-driven exponential smoothing

This article presents an approach to forecasting count time series with a form of exponential smoothing built from observation-driven models. The proposed method is easy to implement and simple to interpret. A variant of the approach is also proposed to handle the impact of outliers on the forecast. The performance of the methodology is studied with simulations and illustrated with an analysis of the number of monthly cases of dengue fever observed in Italy for the years 2008–2021. An R package is made available to enable the reader to reproduce the results discussed in the article.

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来源期刊

Stat Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

1.10

自引率

0.00%

发文量

期刊介绍： Stat is an innovative electronic journal for the rapid publication of novel and topical research results, publishing compact articles of the highest quality in all areas of statistical endeavour. Its purpose is to provide a means of rapid sharing of important new theoretical, methodological and applied research. Stat is a joint venture between the International Statistical Institute and Wiley-Blackwell. Stat is characterised by: • Speed - a high-quality review process that aims to reach a decision within 20 days of submission. • Concision - a maximum article length of 10 pages of text, not including references. • Supporting materials - inclusion of electronic supporting materials including graphs, video, software, data and images. • Scope - addresses all areas of statistics and interdisciplinary areas. Stat is a scientific journal for the international community of statisticians and researchers and practitioners in allied quantitative disciplines.