By Nina Golyandina

During the last 15 years, singular spectrum research (SSA) has confirmed very winning. It has already develop into a customary device in climatic and meteorological time sequence research and renowned in nonlinear physics and sign processing. although, regardless of the promise it holds for time sequence purposes in different disciplines, SSA isn't widely recognized between statisticians and econometrists, and even though the fundamental SSA set of rules appears uncomplicated, knowing what it does and the place its pitfalls lay is in no way simple.Analysis of Time sequence constitution: SSA and similar suggestions presents a cautious, lucid description of its basic thought and method. half I introduces the fundamental thoughts, and units forth the most findings and effects, then offers a close therapy of the method. After introducing the fundamental SSA set of rules, the authors discover forecasting and follow SSA principles to change-point detection algorithms. half II is dedicated to the speculation of SSA. right here the authors formulate and end up the statements of half I. They tackle the singular worth decomposition (SVD) of genuine matrices, time sequence of finite rank, and SVD of trajectory matrices.Based at the authors' unique paintings and jam-packed with purposes illustrated with actual info units, this booklet bargains an excellent chance to acquire a operating wisdom of why, whilst, and the way SSA works. It builds a powerful starting place for effectively utilizing the strategy in purposes starting from arithmetic and nonlinear physics to economics, biology, oceanology, social technological know-how, engineering, monetary econometrics, and marketplace examine.

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Additional info for Analysis of Time Series Structure: SSA and Related Techniques (Chapman & Hall CRC Monographs on Statistics & Applied Probability)

Example text

HN −1 ) (in other words, if Y is the Hankel matrix), then gi = hi for all i. 4) applied to a resultant matrix XIk produces the series (k) (k) (k) F = (f0 , . . , fN −1 ) and therefore the initial series f0 , . . , fN −1 is decomposed into the sum of m series: m fn(k) . 2 Steps in Basic SSA: comments The formal description of the steps in Basic SSA requires some elucidation. In this section we briefly discuss the meaning of the procedures involved. 1 Embedding Embedding can be regarded as a mapping that transfers a one-dimensional time series F = (f0 , .

15) k=1 where N is the length of the series, 0 ≤ n < N , and sN/2 = 0 for even N . The zero term c0 is equal to the average of the series, so that if the series is centred, then c0 = 0. For a series of a finite length, the periodogram of the series is an analogue of the spectral measure. 5) the periodogram ΠN f (ω) of the series F = (f0 , . . , fN −1 ) is ΠN f (ω) = Copyright © 2001 CRC Press, LLC 1 N N −1 n=0 2 e−i2πωn fn , ω ∈ (−1/2, 1/2]. 16) N Since the elements of the series F are real numbers, ΠN f (−ω) = Πf (ω) for |ω| < 1/2, and therefore we can consider only the interval [0, 1/2] for ω.

6) if the coefficients ai are calculated through the whole collection of the lagged vectors (for example, by the least squares method). This invariance under permutation of the lagged vectors is not surprising since both models deal with stationary-like series and are intended for finding global characteristics of the whole series. The number of lagged vectors K plays the role of the ‘sample size’ in these considerations, and therefore it has to be rather large. Theoretically, in these approaches L must be fixed and K → ∞.

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