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Time Series Analysis by State Space Methods: Second Edition
Abstract
This excellent text provides a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbence [sic] terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. The book provides an excellent source for the development of practical courses on time series analysis.
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Metadata
- Authors
- Durbin, J., & Koopman, S. J.
- Affiliations
- Tinbergen Institute (NL)
- ORCID
- Koopman, Siem Jan
- Venue
- Oxford University Press eBooks
- Publisher
- Oxford University Press
- Published
- 2012-05-03
- Pages
- 253
- OpenAlex
- W2147269409
- Cited by
- 3,301
- References
- 0
- Citations by year
- 2026: 167 · 2025: 157 · 2024: 181 · 2023: 183 · 2022: 190 · 2021: 209 · 2020: 173 · 2019: 206 · 2018: 163 · 2017: 158 · 2016: 172 · 2015: 166 · 2014: 152 · 2013: 113 · 2012: 119
- FWCI
- 67.93
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Oxford University Press eBooks (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- ACM Transactions on Spatial Algorithms and Systems
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- state-space model, time series analysis, trend component, seasonal component, time series forecasting
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