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Parameter Estimation, Forecasting, Gap Filling

  • Nina Golyandina
  • Anton Korobeynikov
  • Anatoly Zhigljavsky
Chapter
Part of the Use R! book series (USE R)

Abstract

Chapter 3 is devoted to applications of SSA for one-dimensional series for forecasting, gap filling, low-rank approximation, parameter estimation, and change-point detection. The SSA analysis of time series of Chap.  2 is model-free. Methods of Chap. 3, on the contrary, are model-based. The model is constructed on the base of the approximating subspace built in the process of performing the SSA analysis of Chap.  2. The main parametric model is a linear recurrence relation which the signal should approximately satisfy. Application of methods is illustrated on real-life data.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Nina Golyandina
    • 1
  • Anton Korobeynikov
    • 1
  • Anatoly Zhigljavsky
    • 2
  1. 1.Faculty of Mathematics and MechanicsSaint Petersburg State UniversitySaint PetersburgRussia
  2. 2.School of MathematicsCardiff UniversityCardiffUK

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