Introduction to Time Series and Forecasting 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Introduction to Time Series and Forecasting

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Peter J. Brockwell 作者
Springer
譯者
2010-4-29 出版日期
437 頁數
USD 119.00 價格
Hardcover
Springer Texts in Statistics 叢書系列
9780387953519 圖書編碼

Introduction to Time Series and Forecasting 在線電子書 圖書標籤: Statistics  時間序列  金融數學  數學  教材  金融時間序列  統計學  統計   


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發表於2024-07-03

Introduction to Time Series and Forecasting 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Introduction to Time Series and Forecasting 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Introduction to Time Series and Forecasting 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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當場去世

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當場去世

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B.A.R.

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沒怎麼看,默默匿瞭。

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nice intro

Introduction to Time Series and Forecasting 在線電子書 著者簡介

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 在線電子書 著者簡介


Introduction to Time Series and Forecasting 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Introduction to Time Series and Forecasting 在線電子書 圖書描述

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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