Time Series Analysis and Forecasting, 2/e (Hardcover)

Time series analysis and forecasting have become increasingly important in many fields of research and application. The goal of this book is to distill and integrate current research results into cohesive and comprehensible methodologies, and to provide a streamlined approach to time series analysis and forecasting.

This book is written in a manner which is suitable for a two-semester course in applied time series analysis and forecasting. It can be used in a one-semester course by excluding more advanced topics in the later chapters. The material is targeted to advanced undergraduate and graduate level students in various fields of study (e.g., business, economics, statistics, engineering, medicine, social sciences, environmental sciences, politics, etc.). With its emphasis on practicality and applicability of methodologies, the book is also useful as a reference source for researchers and practitioners in time series analysis and forecasting. While traditional Box-Jenkins methodology emphasizes the exploration and exploitation of serial correlation in time series, the progressive topics on modeling automation, outliers, heteroscedasticity, and nonlinearity are also of great importance. This book places greater emphasis on these newer issues as well as multivariate approaches in time series analysis and forecasting.

Chapters 2 and 3 cover traditional Box-Jenkins ARIMA methodology for non-seasonal and seasonal time series. These two chapters discuss a vast amount of material in a concise manner. In Chapter 4, an effective approach is introduced to automate the task of ARIMA model identification. This approach can be used in conjunction with transfer function models (Chapter 5) to accomplish automatic transfer function modeling. The idea of automation in model building can be further extended to vector ARMA models and simultaneous transfer function (STF) models discussed in Chapters 14 and 15.

Systematic and non-systematic disturbances cause difficulties and inaccuracies in time series modeling and forecasting. Chapter 6 addresses systematic disturbances caused by calendar variation in monthly time series. Chapter 7 discusses non-systematic disturbances relevant to intervention analysis and outlier detection. As an application of outlier detection and estimation, data mining on time series is discussed in Chapter 9. For completeness, traditional forecasting methods using exponential smoothing are addressed in Chapter 8.

Chapter 10 discusses the use of power transformation for modeling and forecasting of certain heteroscedastic time series. In Chapter 11, conditional heteroscedastic models are introduced to address analysis for time series with heteroscedasticity in a different manner. This class of models includes the well-known ARCH/GARCH models.

Traditional time series models often assume that the relationships within the series and the relationships between series are linear. This assumption may not always be appropriate. In Chapter 12, time-segmented and value-segmented (threshold) time series modeling is presented for handling such nonlinear or non-homogenous relationships. Additional topics on nonlinear time series models such as TAR models are discussed in Chapter 13.

Univariate ARIMA models can be generalized to cover multivariate time series. The theory and application of vector ARMA models for multivariate time series analysis are discussed in Chapter 14. Similar to vector ARMA models, single-equation transfer function models can be generalized to multi-equation transfer function models for multivariate time series analysis and econometric analysis. The theory and application of simultaneous transfer function (STF) models are discussed in Chapter 15. As a special application of vector ARMA models, a causality test using this class of models is discussed in Chapter 1

時間序列分析和預測在許多研究和應用領域中變得越來越重要。本書的目標是將當前的研究成果融入到有條理且易於理解的方法中，並提供一種簡化的時間序列分析和預測方法。本書以適用於應用時間序列分析和預測的兩學期課程的方式撰寫，也可以在一學期課程中排除後面章節中的更高級主題。本書的內容針對各個領域的高年級本科生和研究生（例如商業、經濟、統計、工程、醫學、社會科學、環境科學、政治等）進行了定位。由於強調方法的實用性和應用性，本書也可作為時間序列分析和預測的研究人員和從業人員的參考資料。傳統的Box-Jenkins方法強調對時間序列中的序列相關性進行探索和利用，但模型自動化、異常值、異方差性和非線性等進階主題也非常重要。本書更加強調這些新問題以及時間序列分析和預測中的多變量方法。

第2章和第3章介紹了非季節性和季節性時間序列的傳統Box-Jenkins ARIMA方法。這兩章以簡潔的方式討論了大量的材料。在第4章中，介紹了一種有效的方法來自動化ARIMA模型識別的任務。這種方法可以與轉移函數模型（第5章）一起使用，實現自動轉移函數建模。模型構建的自動化思想還可以進一步擴展到向量ARMA模型和同時轉移函數（STF）模型，這些模型在第14章和第15章中討論。

系統性和非系統性干擾導致時間序列建模和預測存在困難和不準確性。第6章討論了由月度時間序列中的日曆變異引起的系統性干擾。第7章討論了與干預分析和異常值檢測相關的非系統性干擾。作為異常值檢測和估計的應用，第9章討論了時間序列的數據挖掘。為了完整起見，第8章還討論了使用指數平滑的傳統預測方法。

第10章討論了使用功率轉換對某些異方差時間序列進行建模和預測的方法。第11章介紹了條件異方差模型，以不同的方式處理具有異方差性的時間序列分析。這類模型包括著名的ARCH/GARCH模型。

傳統的時間序列模型通常假設序列內部的關係和序列之間的關係是線性的。這種假設並不總是適用。第12章介紹了時間分段和值分段（閾值）時間序列建模，用於處理這種非線性或非均質關係。第13章討論了非線性時間序列模型（例如TAR模型）的其他主題。

單變量ARIMA模型可以推廣到多變量時間序列。第14章討論了用於多變量時間序列分析的向量ARMA模型的理論和應用。與向量ARMA模型類似，單方程轉移函數模型可以推廣為多方程轉移函數模型，用於多變量時間序列分析和計量經濟分析。第15章討論了同時轉移函數（STF）模型的理論和應用。作為向量ARMA模型的一個特殊應用，本書還討論了使用這類模型進行因果關係測試的方法（第1章）。