商品描述
This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform.
The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc.
This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book 'Multivariate Time Series With Linear State Space Structure', by the same author, if they require more details.
商品描述(中文翻譯)
這本書介紹了線性單變量和多變量時間序列分析，提供了每個主題的簡要理論見解，並從一開始就用軟體範例來說明理論。因此，它迅速讓讀者從理論和實踐的角度了解每個主題的特點。書中還包含了許多範例和實際應用，展示如何處理不同類型的時間序列數據。相關的軟體包 SSMMATLAB 是用 MATLAB 編寫的，並且也可以在免費的 OCTAVE 平台上運行。
本書專注於使用狀態空間方法的線性時間序列模型，以卡爾曼濾波器和光滑器作為模型估計、預測和信號提取的主要工具。一章關於狀態空間模型描述了這些工具，並提供了它們在一般狀態空間模型中的使用範例。書中還討論了其他主題，包括 ARIMA、傳遞函數和結構模型，以及在單變量情況下使用典型分解進行信號提取，還有在多變量情況下的 VAR、VARMA、協整 VARMA、VARX、VARMAX 和多變量結構模型。它還涉及頻譜分析、在基於模型的方法中使用固定濾波器，以及在存在異常值、干預、複雜季節模式和其他影響（如復活節、交易日等）時，對 ARIMA 和傳遞函數模型的自動模型識別程序。
這本書旨在為處理時間序列的各個領域的學生和研究人員提供指導。該軟體提供了許多自動程序來處理常見的實際情況，但同時，具備程式設計技能的讀者可以編寫自己的程式來解決特定問題。雖然每個主題的理論介紹保持在最低限度，但如果讀者需要更多細節，可以參考同一作者的伴隨書籍《具有線性狀態空間結構的多變量時間序列》。
作者簡介
Dr. Víctor Gómez is a statistician and technical advisor at the Spanish Ministry of Finance and Public Administrations in Madrid. His work involves statistical, econometric and, above all, time series analysis of macroeconomic data, mostly in connection with short-term economic analysis. More recently, he has focused on research in the field of time series analysis and the development of software for time series analysis. He has also taught numerous courses on time series analysis and related topics such as short-term forecasting, seasonal adjustment methods and time series filtering.
作者簡介(中文翻譯)
Víctor Gómez 博士 是西班牙馬德里財政與公共行政部的統計學家和技術顧問。他的工作涉及宏觀經濟數據的統計、計量經濟學，尤其是時間序列分析，主要與短期經濟分析相關。最近，他專注於時間序列分析領域的研究以及時間序列分析軟體的開發。他還教授了多門有關時間序列分析及相關主題的課程，例如短期預測、季節調整方法和時間序列過濾。
