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changes for JOSS publication #13

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minor changes in paper.md
danielskatz Nov 14, 2025
4ff7168
case changes in paper.bib
danielskatz Nov 14, 2025
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36 changes: 18 additions & 18 deletions paper.bib
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@

@book{elsner_singular_1996,
address = {Boston, MA},
title = {Singular {Spectrum} {Analysis}: {A} {New} {Tool} in {Time} {Series} {Analysis}},
title = {Singular Spectrum Analysis: A New Tool in Time Series Analysis},
copyright = {http://www.springer.com/tdm},
isbn = {978-1-4419-3266-2 978-1-4757-2514-8},
url = {http://link.springer.com/10.1007/978-1-4757-2514-8},
Expand All @@ -16,8 +16,8 @@ @book{elsner_singular_1996

@book{golyandina_singular_2020,
address = {Berlin, Heidelberg},
series = {{SpringerBriefs} in {Statistics}},
title = {Singular {Spectrum} {Analysis} for {Time} {Series}},
series = {{SpringerBriefs} in Statistics},
title = {Singular Spectrum Analysis for Time Series},
copyright = {http://www.springer.com/tdm},
isbn = {978-3-662-62435-7 978-3-662-62436-4},
url = {http://link.springer.com/10.1007/978-3-662-62436-4},
Expand Down Expand Up @@ -49,7 +49,7 @@ @article{golyandina_particularities_2020


@article{hassani_singular_2007,
title = {Singular {Spectrum} {Analysis}: {Methodology} and {Comparison}},
title = {Singular Spectrum Analysis: Methodology and Comparison},
volume = {5},
issn = {1680-743X, 1683-8602},
shorttitle = {Singular {Spectrum} {Analysis}},
Expand Down Expand Up @@ -97,9 +97,9 @@ @article{broomhead_extracting_1986
}

@article{allen_monte_1996,
title = {Monte {Carlo} {SSA}: {Detecting} irregular oscillations in the {Presence} of {Colored} {Noise}},
title = {Monte {C}arlo {SSA}: Detecting irregular oscillations in the Presence of Colored Noise},
issn = {1520-0442},
shorttitle = {Monte {Carlo} {SSA}},
shorttitle = {Monte {C}arlo {SSA}},
url = {https://journals.ametsoc.org/view/journals/clim/9/12/1520-0442_1996_009_3373_mcsdio_2_0_co_2.xml},
doi = {10.1175/1520-0442(1996)009<3373:MCSDIO>2.0.CO;2},
language = {en},
Expand Down Expand Up @@ -128,7 +128,7 @@ @article{schreiber_surrogate_2000
}

@article{faouzi_pyts_2020,
title = {{pyts: A Python Package for Time Series Classification}},
title = {{pyts}: A {P}ython Package for Time Series Classification},
volume = {21},
url = {http://jmlr.org/papers/v21/19-763.html},
number = {46},
Expand All @@ -139,7 +139,7 @@ @article{faouzi_pyts_2020
}

@misc{hammad_pyactigraphy_2024,
title = {{pyActigraphy}: {Open}-source python package for actigraphy data visualization and analysis},
title = {{pyActigraphy}: Open-source {P}ython package for actigraphy data visualization and analysis},
url = {https://doi.org/10.5281/zenodo.12163161},
publisher = {Zenodo},
author = {Hammad, Grégory and Reyt, Mathilde and Beliy, Nikita and Baillet, Marion and Deantoni, Michele and Lesoinne, Alexia and Muto, Vincenzo and Schmidt, Christina},
Expand All @@ -149,7 +149,7 @@ @misc{hammad_pyactigraphy_2024
}

@misc{khider_pyleoclim_2023,
title = {Pyleoclim: {A} {Python} package for the analysis and visualization of paleoclimate data},
title = {Pyleoclim: A {P}ython package for the analysis and visualization of paleoclimate data},
url = {https://doi.org/10.5281/zenodo.7523617},
publisher = {Zenodo},
author = {Khider, Deborah and Emile-Geay, Julien and Zhu, Feng and James, Alexander and Landers, Jordan and Kwan, Myron and Athreya, Pratheek},
Expand All @@ -159,7 +159,7 @@ @misc{khider_pyleoclim_2023
}

@misc{halko_finding_2010,
title = {Finding structure with randomness: {Probabilistic} algorithms for constructing approximate matrix decompositions},
title = {Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions},
shorttitle = {Finding structure with randomness},
url = {http://arxiv.org/abs/0909.4061},
doi = {10.48550/arXiv.0909.4061},
Expand All @@ -176,7 +176,7 @@ @misc{halko_finding_2010
@book{golyandina_singular_2018,
address = {Berlin, Heidelberg},
series = {Use {R}!},
title = {Singular {Spectrum} {Analysis} with {R}},
title = {Singular Spectrum Analysis with {R}},
copyright = {http://www.springer.com/tdm},
isbn = {978-3-662-57378-5 978-3-662-57380-8},
url = {http://link.springer.com/10.1007/978-3-662-57380-8},
Expand All @@ -189,17 +189,17 @@ @book{golyandina_singular_2018
}

@inproceedings{seabold_statsmodels_2010,
title = {{Statsmodels: Econometric and Statistical Modeling with Python}},
booktitle = {9th {Python} in {Science} {Conference}},
title = {Statsmodels: Econometric and Statistical Modeling with {P}ython},
booktitle = {9th {P}ython in Science Conference},
author = {Seabold, Skipper and Perktold, Josef},
year = {2010},
doi = {10.25080/Majora-92bf1922-011}
}

@inproceedings{mckinney_data_2010,
title = {Data {Structures} for {Statistical} {Computing} in {Python}},
title = {Data Structures for Statistical Computing in {P}ython},
doi = {10.25080/Majora-92bf1922-00a},
booktitle = {Proceedings of the 9th {Python} in {Science} {Conference}},
booktitle = {Proceedings of the 9th {P}ython in Science Conference},
author = {McKinney, Wes},
editor = {Walt, Stéfan van der and Millman, Jarrod},
year = {2010},
Expand All @@ -221,7 +221,7 @@ @article{harris_array_2020
}

@article{virtanen_scipy_2020,
title = {{SciPy} 1.0: {Fundamental} {Algorithms} for {Scientific} {Computing} in {Python}},
title = {{SciPy} 1.0: Fundamental Algorithms for Scientific Computing in {P}ython},
volume = {17},
doi = {10.1038/s41592-019-0686-2},
journal = {Nature Methods},
Expand All @@ -231,7 +231,7 @@ @article{virtanen_scipy_2020
}

@article{pedregosa_scikit-learn_2011,
title = {Scikit-learn: {Machine} {Learning} in {Python}},
title = {Scikit-learn: Machine Learning in {P}ython},
volume = {12},
journal = {Journal of Machine Learning Research},
author = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
Expand All @@ -241,7 +241,7 @@ @article{pedregosa_scikit-learn_2011
}

@book{durbin_time_2012,
title = {Time {Series} {Analysis} by {State} {Space} {Methods}},
title = {Time Series Analysis by State Space Methods},
isbn = {978-0-19-964117-8},
url = {https://doi.org/10.1093/acprof:oso/9780199641178.001.0001},
abstract = {This book presents a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as being made up of distinct components such as trend, seasonal, regression elements and disturbance elements, each of which is modelled separately. The techniques that emerge from this approach are very flexible. Part I presents a full treatment of the construction and analysis of linear Gaussian state space models. The methods are based on the Kalman filter and are appropriate for a wide range of problems in practical time series analysis. The analysis can be carried out from both classical and Bayesian perspectives. Part I then presents illustrations to real series and exercises are provided for a selection of chapters. Part II discusses approximate and exact approaches for handling broad classes of non-Gaussian and nonlinear state space models. Approximate methods include the extended Kalman filter and the more recently developed unscented Kalman filter. The book shows that exact treatments become feasible when simulation-based methods such as importance sampling and particle filtering are adopted. Bayesian treatments based on simulation methods are also explored.},
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4 changes: 2 additions & 2 deletions paper.md
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Expand Up @@ -84,7 +84,7 @@ a Singular Value Decomposition of the trajectory matrix. The BK-SSA approach is
based on a time-delayed trajectory matrix with dimensions depending on the
window parameter and the number of unit lags. This matrix consists of lagged
copies of time series segments of a specified length, forming a Hankel matrix,
i.e., with equal anti-diagonal values. In contrast, the VG-SSA approach captures
i.e., a matrix with equal anti-diagonal values. In contrast, the VG-SSA approach captures
time dependencies by constructing a special type of covariance matrix that has a
Toeplitz structure, meaning that its diagonal values are identical. The
eigenvalues of the SVD depend on the variance captured by each component, either
Expand All @@ -101,7 +101,7 @@ autoregressive (AR) surrogate time series [@schreiber_surrogate_2000]. Many
extensions have been proposed for the methods, paving the way for future
developments, such as multivariate (or multichannel) SSA (M-SSA),
SSA-based interpolation and extrapolation,
or causality tests [@golyandina_singular_2020].
and causality tests [@golyandina_singular_2020].

# Implementation Details

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