指数平滑预测法：状态空间方法 Forecasting with Exponential Smoothing：The State Space Approach

基本信息/Product Details

Format Paperback | 362 pages

Dimensions 155 x 235 x 22.86mm | 581g

Publication date 03 Sep 2008

Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Edition Statement 2008 ed.

ISBN10 3540719164

ISBN13 9783540719168

页面参数仅供参考，具体以实物为准

书籍简介/Book Description

指数平滑法自20世纪50年代以来一直存在，并且仍然是商业和工业中使用的流行的预测方法。然而，直到近期才开发出一个包含随机模型、似然计算、预测区间和模型选择程序的建模框架。

本书汇集了关于指数平滑的状态空间框架的所有重要的新结果。想在自己感兴趣的领域应用这些方法的人，以及想把这些想法带入新方向的研究人员，都会对本书感兴趣。第一部分介绍了指数平滑和基础模型。第二部分给出了基本细节，还提供了文献中重要的论文的链接。第3部分涵盖了更多的高级主题，包括模型的数学属性和对特定问题的模型扩展。第四部分讨论了在特定领域的应用。

Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. This book brings together all of the important new results on the state space framework for exponential smoothing. It will be of interest to people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions. Part 1 provides an introduction to exponential smoothing and the underlying models. The essential details are given in Part 2, which also provide links to the most important papers in the literature. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems. Applications to particular domains are discussed in Part 4.

目录/Table of contents

I. Introduction: Basic concepts.- Getting started. II. Essentials: Linear innovations state space models.- Non-linear and heteroscedastic innovations state space models.- Estimation of innovations state space models.- Prediction distributions and intervals.- Selection of models. III. Further topics: Normalizing seasonal components.- Models with regressor variables.- Some properties of linear models.- Reduced forms and relationships with ARIMA models.- Linear innovations state space models with random seed states.- Conventional state space models.- Time series with multiple seasonal patterns.- Non-linear models for positive data.- Models for count data.- Vector exponential smoothing. IV. Applications: Inventory control application.- Conditional heteroscedasticity and finance applications.- Economic applications: the Beveridge-Nelson decomposition.

作者简介/Author

罗宾·约翰·亨德曼（Robin John Hyndman FAA FASSA）（生于1967年5月2日）是一位澳大利亚统计学家，因其在预测和时间序列方面的工作而闻名。他是莫纳什大学的统计学教授，并在2005-2018年期间担任《国际预测杂志》的主编。2007年，他因对统计研究的贡献而获得澳大利亚科学院颁发的莫兰奖章。

亨德曼是独立于尺度的预测误差测量指标平均绝对尺度误差（MASE）的共同创造者和倡导者。常见的预测误差指标，如平均绝对误差、几何平均绝对误差和平均平方误差，都有与依赖数据的尺度和/或处理数据中的零和负值有关的缺点。Hyndman的MASE指标解决了这些问题，并且可以在任何预测生成方法下使用。由于其无标度特性，它可以在不同模型之间进行比较。

亨德曼在墨尔本大学学习统计学和数学，在那里他以一级荣誉获得了理学士学位和博士学位。

Robin John Hyndman FAA FASSA (born 2 May 1967) is an Australian statistician known for his work on forecasting and time series. He is Professor of Statistics at Monash University and was Editor-in-Chief of the International Journal of Forecasting from 2005–2018. In 2007 he won the Moran Medal from the Australian Academy of Science for his contributions to statistical research. In 2021 he won the Pitman Medal from the Statistical Society of Australia.

Hyndman is co-creator and proponent of the scale-independent forecast error measurement metric mean absolute scaled error (MASE). Common metrics of forecast error, such as mean absolute error, geometric mean absolute error, and mean squared error, have shortcomings related to dependence on scale of data and/or handling zeros and negative values within the data. Hyndman's MASE metric resolves these and can be used under any forecast generation method.[6] It allows for comparison between models due to its scale-free property.

Hyndman studied statistics and mathematics at the University of Melbourne, where he earned a Bachelor of Science with first class honours and a PhD.[1] He was elected Fellow of the Academy of the Social Sciences in Australia in 2020, and Fellow of the Australian Academy of Science in 2021