The "Caterpillar"-SSA method for analysis of time series with missing values

By Nina Golyandina, E. Osipov. 2007

148 citations recorded by the source corpus.

Subfields: Applied Mathematics, Cardiology and Cardiovascular Medicine

View Paper: https://www.semanticscholar.org/paper/57e7ce93c4d682a5d57fe8ac3122162f0e2cf346

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In Applied Mathematics

1. Hossein Hassani
2. Anatoly A. Zhigljavsky
3. Bofeng Li
4. Yunzhong Shen
5. Fukai Peng
6. Nina Golyandina
7. Alireza Amiri-Simkooei
8. X. Fang

In Cardiology and Cardiovascular Medicine

1. Hossein Hassani
2. Anatoly A. Zhigljavsky
3. Yunzhong Shen
4. Bofeng Li
5. Fukai Peng
6. Nina Golyandina

Citations in the corpus

• Transformer Networks for Trajectory Forecasting (2020, background): “tracking [44]), or forecasting of heterogeneous data [45], [46], [47], [48], [49], where in general is treated by designing ad-hoc extensions for filling”
• Singular Spectrum Analysis for Time Series (2013, methodology): “In addition to forecasting, the Basic SSA can be used for smoothing, filtration, noise reduction, extraction of trends of different resolution, extraction of periodicities in the form of modulated harmonics, gap-filling [8,9] and other tasks, see [3].”
• Singular Spectrum Analysis: Methodology and Comparison (2021, methodology): “There are different SSA-based methods for filling in missing data sets (see, for example, Schoellhamer, 2001; Kondrashov et al., 2005; Golyandina and Osipov, 2006; Kondrashov, 2006).”
• A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, methodology): “Iterative Caterpillar Singular Spectrum Analysis Method (ICSSA) is a modification of the CSSA (Golyandina and Osipov, 2007) method developed to describe time series and fill missing data by decomposing the time series into empirical orthogonal functions (EOF).”
• Improved singular spectrum analysis for time series with missing data (2014, methodology): “…kind of SSA approaches pro-10 cess the time series with missing data by filling the data gaps recursively or iteratively, such as the “Catterpillar”-SSA method (Golyandina and Osipov, 2007), the imputation method (Rodrigues and Carvalho, 2013) or the iterative method (Kondrashov and Ghil, 2006).”