Introduction to Time Series and Forecasting 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Introduction to Time Series and Forecasting

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Peter J. Brockwell 作者
Springer
译者
2010-4-29 出版日期
437 页数
USD 119.00 价格
Hardcover
Springer Texts in Statistics 丛书系列
9780387953519 图书编码

Introduction to Time Series and Forecasting 在线电子书 图书标签: Statistics  时间序列  金融数学  数学  教材  金融时间序列  统计学  统计   


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发表于2024-07-06


Introduction to Time Series and Forecasting 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Introduction to Time Series and Forecasting 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Introduction to Time Series and Forecasting 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



Introduction to Time Series and Forecasting 在线电子书 用户评价

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best intro to time series from a mathematician's perspective. vastly better than the Hamilton book.

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STAT 429

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nice intro

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B.A.R.

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nice intro

Introduction to Time Series and Forecasting 在线电子书 著者简介

Peter J. Brockwell and Richard A. Davis are Fellows of the American Statistical Association and the Institute of Mathematical Statistics and elected members of the International Statistics institute. Richard A. Davis is the current President of the Institute of Mathematical Statistics and, with W.T.M. Dunsmuir, winner of the Koopmans Prize. Professors Brockwell and Davis are coauthors of the widely used advanced text, Time Series: Theory and Methods, Second Edition (Springer-Verlag, 1991).


Introduction to Time Series and Forecasting 在线电子书 图书目录


Introduction to Time Series and Forecasting 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Introduction to Time Series and Forecasting 在线电子书 图书描述

Some of the key mathematical results are stated without proof in order to make the underlying theory accessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and nonstationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introductions are also given to cointegration and to nonlinear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

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