Time Series Decomposition and Seasonal Adjustment

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Time Series Decomposition and Seasonal Adjustment

Zong, Ping

ウェブストア価格 ¥29,961（本体¥27,238）
CRC Press（2026/03発売）
外貨定価 US$ 140.00
読書週間ポイント2倍キャンペーン対象商品（～11/9）
ポイント 544pt

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製本 Hardcover:ハードカバー版／ページ数 360 p.
言語 ENG
商品コード 9781041204619

Full Description

This book provides an in-depth examination of time series decomposition and seasonal adjustment, focusing on the X-13ARIMA-SEATS and TRAMO-SEATS methods. Seasonal adjustment removes distortions such as seasonal fluctuations and holiday effects from economic indicators (eg, GDP, CPI), enabling clearer insights into underlying trends, cycles, and shocks. These tools are vital for sound policymaking, accurate forecasting, and reliable international comparisons.

X-13ARIMA-SEATS, developed by the U.S. Census Bureau, combines empirical moving average filters with ARIMA/regARIMA modelling to handle outliers, calendar effects, and endpoint issues. TRAMO-SEATS, created by the Bank of Spain, uses a model-based strategy: TRAMO pre-adjusts data with ARIMA models, while SEATS applies signal extraction to decompose components. X-13ARIMA-SEATS excels with stable seasonal patterns, while TRAMO-SEATS provides rigorous solutions for complex holiday structures.

The book also examines modern challenges, including structural breaks from COVID-19, high-frequency data with multiple seasonalities, and the demand for real-time adjustments. It reviews innovations such as hybrid models combining machine learning with traditional filters, Bayesian state-space approaches, and adaptive methods like Kalman filters.

Intended for students, researchers, staff at national statistical agencies, central banks, and financial institutions, the book equips readers with methodological and practical tools to navigate evolving economic data landscapes.

List of Figures List of Tables Preface The Book Highlights 1 Introduction 2 Evolution of X-11 Family Seasonal Adjustment Methods 3 The Conceptual Framework for the General Theory of Seasonal Adjustment 4 X-11 Method Based on Moving Averages 5 Application of X-13ARIMA-SEATS Software 6 X-13ARIMA-SEATS Advanced Topics and Practice 7 TRAMO-SEATS: A Model-Based Time Series Decomposition Method 8 JDemetra+: Advanced Seasonal Adjustment Tools and Applications Chapter 9: Challenges and Future Directions of Seasonal Adjustment Methods BIBLIOGRAPHY