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Singular Spectrum Analysis for Time Series

Part of the SpringerBriefs in Statistics Book Series

DOI: 10.1007/978-3-642-34913-3

ISBN: 978-3-642-34912-6

Publisher: Springer Berlin Heidelberg - Springer Journals

Jan 19, 2013

Available

Introduction

Golyandina, Nina; Zhigljavsky, Anatoly

Pages: 1 –10

[Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing.]
Available

Basic SSA

Golyandina, Nina; Zhigljavsky, Anatoly

Pages: 11 –70

[Consider a real-valued time series X=XN=(x1,…,xN)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt}...
Available

SSA for Forecasting, Interpolation, Filtration and Estimation

Golyandina, Nina; Zhigljavsky, Anatoly

Pages: 71 –119

[A reasonable forecast of a time series can be performed only if the series has a structure and there are tools to identify and use this structure. Also, we should assume that the structure of the time series is preserved for the future time period over which we are going to forecast (continue)...

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