Singular spectrum analysis based on the perturbation theory

DOI10.1016/J.NONRWA.2011.03.020zbMath1225.94006OpenAlexW1987098267MaRDI QIDQ635237

Zhengyuan Xu, Hossein Hassani, Anatoly A. Zhigljavsky

Publication date: 19 August 2011

Published in: Nonlinear Analysis. Real World Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.nonrwa.2011.03.020

zbMATH Keywords

perturbation theoryforecastingreconstructionsingular spectrum analysis

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical methods; risk measures (91G70) Inference from stochastic processes and spectral analysis (62M15) Economic time series analysis (91B84) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (6)

Dynamical analysis and perturbation solution of an SEIR epidemic model ⋮ selection of window length for singular spectrum analysis ⋮ Fault-tolerant filter design with quantized measurements ⋮ Multivariate posterior singular spectrum analysis ⋮ Forecasting before, during, and after recession with singular spectrum analysis ⋮ A novel signal extraction approach for filtering and forecasting noisy exponential series

Uses Software

• itsmr
• CaterpillarSSA

Cites Work

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• Perturbation expansions of signal subspaces for long signals
• A review on singular spectrum analysis for economic and financial time series
• Extracting fetal heart signal from noisy maternal ECG by multivariate singular spectrum analysis
• Predicting daily exchange rate with singular spectrum analysis
• Singular spectrum analysis based on the minimum variance estimator
• Extracting qualitative dynamics from experimental data
• Noise in unspecified, nonlinear time series
• Entropy-based independence test
• Signal enhancement-a composite property mapping algorithm
• USING THE MUTUAL INFORMATION COEFFICIENT TO IDENTIFY LAGS IN NONLINEAR MODELS
• A test for independence based on the correlation dimension
• Introduction to Time Series and Forecasting

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