A01=Anatoly A Zhigljavsky
A01=Nina Golyandina
A01=Vladimir Nekrutkin
advanced time series decomposition techniques
Author_Anatoly A Zhigljavsky
Author_Nina Golyandina
Author_Vladimir Nekrutkin
Category=PBT
change-point detection
climate data modeling
component
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
length
linear
matrices
matrix
nonlinear time series
orthonormal
principal
signal processing methods
singular value decomposition
space
trajectory
trajectory matrix analysis
window
Product details
- ISBN 9781584881940
- Weight: 750g
- Dimensions: 156 x 234mm
- Publication Date: 23 Jan 2001
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Over the last 15 years, singular spectrum analysis (SSA) has proven very successful. It has already become a standard tool in climatic and meteorological time series analysis and well known in nonlinear physics and signal processing. However, despite the promise it holds for time series applications in other disciplines, SSA is not widely known among statisticians and econometrists, and although the basic SSA algorithm looks simple, understanding what it does and where its pitfalls lay is by no means simple.
Analysis of Time Series Structure: SSA and Related Techniques provides a careful, lucid description of its general theory and methodology. Part I introduces the basic concepts, and sets forth the main findings and results, then presents a detailed treatment of the methodology. After introducing the basic SSA algorithm, the authors explore forecasting and apply SSA ideas to change-point detection algorithms. Part II is devoted to the theory of SSA. Here the authors formulate and prove the statements of Part I. They address the singular value decomposition (SVD) of real matrices, time series of finite rank, and SVD of trajectory matrices.
Based on the authors' original work and filled with applications illustrated with real data sets, this book offers an outstanding opportunity to obtain a working knowledge of why, when, and how SSA works. It builds a strong foundation for successfully using the technique in applications ranging from mathematics and nonlinear physics to economics, biology, oceanology, social science, engineering, financial econometrics, and market research.
