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REVIEW 3 major objections 3 minor 64 references

The R-index: A universal metric for evaluating OAM content and mode purity in optical fields

T0 review · 3 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

Pith's one-line read The paper introduces the R-index, a scalar metric it claims captures the intrinsic orbital angular momentum content and mode purity of any structured optical field.

desk verdict Review package is broken: the supplied full text is an unrelated time-series paper, so the R-index is undefined; the abstract's claim is interesting but unverifiable in this form. read the letter →

arxiv 2508.12973 v1 pith:GUDPL3L3 submitted 2025-08-18 physics.optics physics.app-ph

classification physics.opticsphysics.app-ph
keywords orbitalangularmomentumstructuredlightmodepurityopticalvorticesLaguerre-GaussianmodesOAMspectrumfigureofmerit
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces the R-index, a scalar metric meant to serve as a universal measure of orbital angular momentum (OAM) for any structured optical field, from pure Laguerre-Gaussian modes to superpositions containing many vortices. Existing OAM measures stumble when vortices are local and no common rotation axis exists; the R-index claims to resolve this by condensing the field's OAM content into a single figure of merit that also reports how pure the mode is. If it works, it would give experimenters a standard way to compare beams, quantify total OAM, and judge generation fidelity across different optical setups.

What carries the argument

The R-index, a named scalar metric constructed from the OAM (azimuthal index) content of the field's modal decomposition. It is the central object of the method: it does double duty as a total-OAM quantifier and as a purity/fidelity indicator, allowing direct comparison across beam profiles.

What would settle it

Take two fields engineered to have the same R-index: a pure high-order Laguerre-Gaussian beam and a multi-vortex superposition tuned to the same value. Measure the mechanical torque each exerts on a trapped particle, or compare their transformation through a cylindrical-lens mode converter. If the two fields exert measurably different torques or convert differently while sharing the same R-index, the metric does not capture total OAM.

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Extended reading notes

Core claim

The paper's claim is that an R-index defined from the field's OAM spectrum captures intrinsic OAM content in a universal way: for a pure Laguerre-Gaussian mode it reduces to the known integer azimuthal index, while for arbitrary multi-vortex superpositions it returns a single value that quantifies total OAM and simultaneously measures mode purity. The metric is intended to be independent of basis and axis choices, resolving the ambiguity that previously made total OAM difficult to quantify in fields lacking a common rotation axis.

Load-bearing premise

A single scalar index can faithfully represent a structured field's total intrinsic orbital angular momentum, independent of the choice of decomposition basis or axis, even when the field has no single common rotation axis.

Editorial extensions

If this is right

  • The R-index gives a single comparable OAM value for beams with different mode structures, including those with no common rotation axis.
  • It doubles as a purity metric, letting experimentalists quantify the fidelity and robustness of OAM generation.
  • It enables direct comparison across diverse beam profiles and helps identify optimal configurations for applications.
  • It extends OAM quantification beyond single-axis beams to arbitrary multi-vortex fields, unifying OAM characterization into one figure of merit.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same index framework could plausibly transfer to any wave field carrying OAM, such as acoustic or electron vortex beams, since the underlying structure is a modal spectrum.
  • If the R-index becomes standard, it could serve as an optimization target in OAM-multiplexed communication links, where mode purity directly affects channel crosstalk.
  • A testable extension: check whether the R-index responds monotonically to vortex creation or annihilation events in dynamical fields, linking it to topological event rates.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. The manuscript, as identified by its title and abstract, claims to introduce the R-index, a universal metric that captures the intrinsic orbital angular momentum (OAM) content of structured optical fields, from pure Laguerre-Gaussian modes to arbitrary multi-vortex superpositions, and simultaneously quantifies field purity. The abstract further claims that this metric resolves the difficulty of defining total OAM when no common rotation axis exists. However, the full text supplied for review is a different paper: a statistics manuscript on self-supervised denoising of autoregressive models with additive noise, including infinite-variance alpha-stable cases (Stable-N2N). That text contains no definition of the R-index, no OAM formalism, no derivation of any invariance or reduction property, and no optical validation. The central scientific claim of the abstract is therefore unsupported by any verifiable content in the submitted manuscript.

Significance. If the R-index were properly defined and shown to be well-defined, the contribution could be significant: a single scalar that quantifies OAM content and purity for structured fields without a common rotation axis would be useful across optical manipulation, communications, and quantum information. The claimed scope is broad and the proposed unification is attractive. However, the submitted manuscript provides none of the necessary substance: no definition, no mathematical derivation, no checks against known OAM values, and no experiments or simulations on optical fields. There are no machine-checked proofs or reproducible optical results to credit. As it stands, the significance claim is unfalsifiable in this manuscript because the object being claimed does not appear anywhere in the submitted text.

major comments (3)
  1. [Abstract and entire body text] The R-index is never defined. The abstract asserts that it 'captures the intrinsic OAM content of any structured optical field' and 'quantifies total OAM' and 'assesses field purity,' but the body text is arXiv:2508.12970, a statistics paper on denoising alpha-stable autoregressive models. No equation, definition, or theorem in Sections 1-5 or the appendices concerns OAM or an R-index. The central claim of the paper is therefore absent from the submitted manuscript.
  2. [Abstract, second sentence] The paper does not provide the load-bearing well-definedness checks that the abstract's 'universal' claim requires. In particular, there is no demonstration that the proposed metric reduces to the azimuthal index for pure Laguerre-Gaussian modes, is independent of the choice of axis or basis for multi-vortex superpositions, remains finite for arbitrary superpositions, or is distinguished from existing OAM measures. The abstract itself states that 'the absence of a common rotation axis make[s] the total OAM of the field difficult to quantify,' so the manuscript must show how the R-index resolves this ambiguity. None of this appears.
  3. [Section 4 (Simulation study) and Tables 1-5] There is no validation of the claimed OAM metric. The Monte Carlo simulations, box plots, and MAE tables concern denoising of Gaussian and alpha-stable autoregressive time series, not optical fields or OAM content. No comparison is made with existing mode-purity or OAM measures. The empirical content of the manuscript is relevant to a different problem and cannot be used to support the abstract's claims about structured light.
minor comments (3)
  1. [Title and metadata] The title and abstract refer to arXiv:2508.12973 (physics.optics), while the body carries the identifier arXiv:2508.12970v2 [stat.ME] and a title about self-supervised denoising. The metadata must be reconciled.
  2. [Nomenclature and keywords] The nomenclature list and keywords contain only time-series terms (AR, SαS-AR, FLOC, YW, MAE, EIV, Stable-N2N). There are no entries for OAM, vortex, Laguerre-Gaussian, mode purity, or structured light, which underscores that the body does not address the advertised topic.
  3. [Figures and tables] All figures and tables in the body pertain to denoising trajectories and parameter estimation. Captions such as those for Figures 3-18 and Tables 1-5 contain no OAM-related content. This is a presentation mismatch that should be fixed in any corrected submission.

Circularity Check

0 steps flagged · score 0.0 of 10

Cannot assess circularity: supplied full text is arXiv:2508.12970 (a statistics paper), not the R-index paper, so no derivation of the R-index exists to trace.

full rationale

The abstract supplied for arXiv:2508.12973 defines the R-index and claims it 'captures the intrinsic OAM content of any structured optical field,' but the full text provided is a different manuscript: arXiv:2508.12970v2, 'A self-supervised learning approach for denoising autoregressive models with additive noise: finite and infinite variance cases' by Banerjee, Wyłomańska, and Sundar. That text contains no definition of the R-index, no OAM formalism, no Laguerre-Gaussian analysis, and no derivation of the claimed universality or reduction to known OAM values. Circularity analysis requires a concrete derivation chain with equations or fitted parameters to compare; here the central object (R) is entirely absent from the supplied manuscript, so no specific step can be quoted as reducing to its own inputs, by self-citation, or by definition. Honest non-finding: no circularity is identifiable from the evidence provided. The absence of the defining equations is an evidence gap, not a demonstration of circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 1 invented entities

Based on the abstract alone, the novel object is the R-index, and the key postulates are the well-definedness of intrinsic OAM for axis-less multi-vortex fields and the sufficiency of a scalar summary. No free parameters can be identified without the manuscript.

assumptions (2)
  • domain assumption Intrinsic OAM content is a well-defined quantity for structured optical fields without a common rotation axis.
    The abstract motivates the R-index by noting that the local nature of vortex OAM and the absence of a common rotation axis make total OAM difficult to quantify; the paper's solution presumes such a quantity exists.
  • ad hoc to paper A single scalar index (the R-index) can represent total OAM and purity across arbitrary multi-vortex superpositions.
    This is the central postulate of the paper: the abstract asserts the R-index unifies OAM characterization into one figure of merit without showing the definition.
invented entities (1)
  • R-index
    purpose: To quantify intrinsic orbital angular momentum content and mode purity of arbitrary structured optical fields.
    The abstract introduces the R-index as a new metric but provides no falsifiable prediction or external observable; its validity rests entirely on the (unavailable) manuscript.

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Cite this review

Pith. "Pith review of The R-index: A universal metric for evaluating OAM content and mode purity in optical fields." pith.science (2026). https://pith.science/paper/GUDPL3L3

@misc{pith2026250812973,
  author       = {Pith},
  title        = {Pith review of: The R-index: A universal metric for evaluating OAM content and mode purity in optical fields},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GUDPL3L3}},
  note         = {Machine review of arXiv:2508.12973}
}
read the original abstract

Despite its pivotal role in optical manipulation, high-capacity communications, and quantum information, a general measure of orbital angular momentum (OAM) in structured light remains elusive. In optical fields, where multiple vortices coexist, the local nature of vortex OAM and the absence of a common rotation axis make the total OAM of the field difficult to quantify. Here, we introduce the R-index, a metric that captures the intrinsic OAM content of any structured optical field, from pure Laguerre-Gaussian modes to arbitrary multi-vortex superpositions. Not only does this metric quantify the total OAM, it also assesses field purity, providing insight into the fidelity and robustness of the OAM generation. By unifying OAM characterization into a single figure of merit, the R-index enables direct comparison across diverse beam profiles and facilitates the identification of optimal configurations for both foundational studies and applied technologies.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

64 extracted references · 51 canonical work pages

  1. [1]

    S. M. Kay, Modern spectral estimation, Pearson Education India, 1988

  2. [2]

    Legrand, E

    L. Legrand, E. Grivel, Jeffrey’s divergence between autoregressive processes disturbed by additive white noises, Signal Processing 149 (2018) 162–178.doi:10.1016/j.sigpro.2018.03.017

  3. [3]

    Ganapathy, Multivariate Autoregressive Spectrogram Modeling for Noisy Speech Recognition, IEEE Signal Process- ing Letters 24 (9) (2017) 1373–1377.doi:10.1109/lsp.2017.2724561

    S. Ganapathy, Multivariate Autoregressive Spectrogram Modeling for Noisy Speech Recognition, IEEE Signal Process- ing Letters 24 (9) (2017) 1373–1377.doi:10.1109/lsp.2017.2724561

  4. [4]

    Lohani, R

    A. Lohani, R. Kumar, R. Singh, Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques, Journal of Hydrology 442–443 (2012) 23–35.doi:10.1016/j.jhydrol.2012. 03.031

  5. [5]

    Terzi, G

    O. Terzi, G. Ergin, Forecasting of monthly river flow with autoregressive modeling and data-driven techniques, Neural Computing and Applications 25 (1) (2013) 179–188.doi:10.1007/s00521-013-1469-9

  6. [6]

    Marcellino, J

    M. Marcellino, J. H. Stock, M. W. Watson, A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series, Journal of Econometrics 135 (1–2) (2006) 499–526.doi:10.1016/j.jeconom.2005.07.020

  7. [7]

    Weron, A

    R. Weron, A. Misiorek, Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models, International Journal of Forecasting 24 (4) (2008) 744–763.doi:10.1016/j.ijforecast.2008.08.004

  8. [8]

    Kabaˇ sinskas, S

    A. Kabaˇ sinskas, S. T. Rachev, L. Sakalauskas, W. Sun, I. Belovas, Alpha-stable paradigm in financial markets, Journal of Computational Analysis and Applications 11 (4) (2009) 641 – 668. URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77954649972&partnerID=40&md5= 8e22ecfdb772a192994dfa822df84a43

Show all 64 references
  1. [9]

    J. P. Nolan, Modeling Financial Data with Stable Distributions, Elsevier, 2003, p. 105–130.doi:10.1016/ b978-044450896-6.50005-4

  2. [10]

    Nikias, M

    C. Nikias, M. Shao, Recent Advances in Signal Processing with�-Stable Distributions, IF AC Proceedings Volumes 27 (8) (1994) 65–70.doi:10.1016/s1474-6670(17)47693-2

  3. [11]

    J. H. McCulloch, 13 Financial applications of stable distributions, Elsevier, 1996, p. 393–425.doi:10.1016/ s0169-7161(96)14015-3

  4. [12]

    G. ˙Zak, M. Teuerle, A. Wy loma´ nska, R. Zimroz, Measures of Dependence for�-Stable Distributed Processes and Its Application to Diagnostics of Local Damage in Presence of Impulsive Noise, Shock and Vibration 2017 (2017) 1–9. doi:10.1155/2017/1963769

  5. [13]

    X. Yuan, J. Li, E. E. Kuruoglu, Robustness enhancement in neural networks with alpha-stable training noise, Digital Signal Processing 156 (2025) 104778.doi:10.1016/j.dsp.2024.104778

  6. [14]

    Diversi, R

    R. Diversi, R. Guidorzi, U. Soverini, A noise-compensated estimation scheme for AR processes, in: Proceedings of the 44th IEEE Conference on Decision and Control, 2005, pp. 4146–4151.doi:10.1109/CDC.2005.1582811

  7. [15]

    Diversi, R

    R. Diversi, R. Guidorzi, U. Soverini, Identification of autoregressive models in the presence of additive noise, Interna- tional Journal of Adaptive Control and Signal Processing 22 (5) (2007) 465–481.doi:10.1002/acs.989

  8. [16]

    Esfandiari, S

    M. Esfandiari, S. A. Vorobyov, M. Karimi, New estimation methods for autoregressive process in the presence of white observation noise, Signal Processing 171 (2020) 107480.doi:10.1016/j.sigpro.2020.107480

  9. [17]

    ˙Zu lawi´ nski, A

    W. ˙Zu lawi´ nski, A. Grzesiek, R. Zimroz, A. Wy loma´ nska, Identification and validation of periodic autoregressive model with additive noise: finite-variance case, Journal of Computational and Applied Mathematics 427 (2023) 115131. doi:10.1016/j.cam.2023.115131

  10. [18]

    ˙Zu lawi´ nski, A

    W. ˙Zu lawi´ nski, A. Wy loma´ nska, Empirical study of periodic autoregressive models with additive noise – estimation and testing, Communications in Statistics - Simulation and Computation (2023) 1–26.doi:10.1080/03610918.2023. 2286217

  11. [19]

    ˙Zu lawi´ nski, A

    W. ˙Zu lawi´ nski, A. Wy loma´ nska, R. Zimroz, Yule-Walker-Based Approaches for Estimation of Noise-Corrupted Periodic Autoregressive Model - Finite- and Infinite-Variance Cases, in: 2023 31st European Signal Processing Conference (EUSIPCO), IEEE, 2023, p. 1978–1982.doi:10.23...

  12. [20]

    ˙Zu lawi´ nski, A

    W. ˙Zu lawi´ nski, A. Wy loma´ nska, Errors-in-Variables-Based Methodology of Estimation and Testing for Infinite-Variance Periodic Autoregressive Models with Additive Noise, in: 2024 32nd European Signal Processing Conference (EU- SIPCO), IEEE, 2024, p. 1087–1091.doi:10.23919...

  13. [21]

    Y. Li, C. Xu, L. Yi, R. Fang, A data-driven approach for denoising GNSS position time series, Journal of Geodesy 92 (8) (2017) 905–922.doi:10.1007/s00190-017-1102-2

  14. [22]

    837–841.doi:10.1109/WCICA.2018.8630485

    Wu, Yuxuan and Zeng, Ming and Ma, Wenxin and Ma, Jinyu and Zhao, Chunyu, Time series denoising based on empirical mode decomposition and dictionary learning, in: 2018 13th World Congress on Intelligent Control and Automation (WCICA), 2018, pp. 837–841.doi:10.1109/WCICA.2018.8630485

  15. [23]

    Sameni, M

    R. Sameni, M. B. Shamsollahi, C. Jutten, G. D. Clifford, A Nonlinear Bayesian Filtering Framework for ECG Denoising, IEEE Transactions on Biomedical Engineering 54 (12) (2007) 2172–2185.doi:10.1109/TBME.2007.897817

  16. [24]

    J. Gao, H. Sultan, J. Hu, W.-W. Tung, Denoising Nonlinear Time Series by Adaptive Filtering and Wavelet Shrinkage: A Comparison, IEEE Signal Processing Letters 17 (3) (2010) 237–240.doi:10.1109/LSP.2009.2037773

  17. [25]

    T. Chen, J. Morris, E. Martin, Dynamic data rectification using particle filters, Computers & Chemical Engineering 32 (3) (2008) 451–462.doi:10.1016/j.compchemeng.2007.03.012

  18. [26]

    P. Hao, O. Karaku¸ s, A. Achim, Robust Kalman Filters Based on the Sub-Gaussian�-Stable Distribution, preprint, arXiv:2305.07890 [eess.SP] (2023).doi:10.48550/arXiv.2305.07890

  19. [27]

    F. M. Bayer, A. J. Kozakevicius, R. J. Cintra, An iterative wavelet threshold for signal denoising, Signal Processing 162 (2019) 10–20.doi:10.1016/j.sigpro.2019.04.005

  20. [28]

    Gharesi, M

    N. Gharesi, M. M. Arefi, R. Razavi-Far, J. Zarei, S. Yin, A neuro-wavelet based approach for diagnosing bearing defects, Advanced Engineering Informatics 46 (2020) 101172.doi:10.1016/j.aei.2020.101172

  21. [29]

    R. M. Alrumaih, M. A. Al-Fawzan, Time Series Forecasting Using Wavelet Denoising an Application to Saudi Stock 36 Index, Journal of King Saud University - Engineering Sciences 14 (2) (2002) 221–233.doi:10.1016/s1018-3639(18) 30755-4

  22. [30]

    Frusque, O

    G. Frusque, O. Fink, Robust time series denoising with learnable wavelet packet transform, Advanced Engineering Informatics 62 (2024) 102669.doi:10.1016/j.aei.2024.102669

  23. [31]

    L. Fan, F. Zhang, H. Fan, C. Zhang, Brief review of image denoising techniques, Visual Computing for Industry, Biomedicine, and Art 2 (1) (Jul. 2019).doi:10.1186/s42492-019-0016-7

  24. [32]

    Vasilyeva, A

    M. Vasilyeva, A. Krasnikov, K. Gajamannage, M. Mehrubeoglu, Multiscale method for image denoising using nonlinear diffusion process: Local denoising and spectral multiscale basis functions, Journal of Computational and Applied Mathematics 470 (2025) 116733.doi:10.1016/j.cam.20...

  25. [33]

    Zhang, W

    K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang, Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising, IEEE Transactions on Image Processing 26 (7) (2017) 3142–3155.doi:10.1109/TIP.2017.2662206

  26. [34]

    Mansour, K

    Y. Mansour, K. Lin, R. Heckel, Image-to-Image MLP-mixer for Image Reconstruction, preprint, arXiv:2202.02018 [cs.CV] (2022).doi:10.48550/arXiv.2202.02018

  27. [35]

    Z. Tu, H. Talebi, H. Zhang, F. Yang, P. Milanfar, A. Bovik, Y. Li, MAXIM: Multi-Axis MLP for Image Processing, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 5769– 5780. URLhttps://openaccess.thecvf.com/content/CVPR2022/...

  28. [36]

    Lehtinen, J

    J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, T. Aila, Noise2Noise: Learning Image Restoration without Clean Data, in: J. Dy, A. Krause (Eds.), Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine L...

  29. [37]

    J. Xu, Y. Huang, M.-M. Cheng, L. Liu, F. Zhu, Z. Xu, L. Shao, Noisy-as-Clean: Learning Self-Supervised Denoising From Corrupted Image, IEEE Transactions on Image Processing 29 (2020) 9316–9329.doi:10.1109/tip.2020.3026622

  30. [38]

    Moran, D

    N. Moran, D. Schmidt, Y. Zhong, P. Coady, Noisier2Noise: Learning to Denoise From Unpaired Noisy Data, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. URLhttps://openaccess.thecvf.com/content_CVPR_2020/html/Moran_Noisier2Noise_Learning_to_Denoi...

  31. [39]

    Kay, Noise compensation for autoregressive spectral estimates, IEEE Transactions on Acoustics, Speech, and Signal Processing 28 (3) (1980) 292–303.doi:10.1109/tassp.1980.1163406

    S. Kay, Noise compensation for autoregressive spectral estimates, IEEE Transactions on Acoustics, Speech, and Signal Processing 28 (3) (1980) 292–303.doi:10.1109/tassp.1980.1163406

  32. [40]

    ˙Zu lawi´ nski, P

    W. ˙Zu lawi´ nski, P. Kruczek, A. Wy loma´ nska, Alternative dependency measures-based approach for estimation of the �–stable periodic autoregressive model, Communications in Statistics - Simulation and Computation 53 (3) (2022) 1188–1215.doi:10.1080/03610918.2022.2037640

  33. [41]

    Zhang, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing 50 (2003) 159–175.doi:10.1016/s0925-2312(01)00702-0

    G. Zhang, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing 50 (2003) 159–175.doi:10.1016/s0925-2312(01)00702-0

  34. [42]

    Khashei, M

    M. Khashei, M. Bijari, A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, Applied Soft Computing 11 (2) (2011) 2664–2675.doi:10.1016/j.asoc.2010.10.015

  35. [43]

    A. M. Sathe, N. S. Upadhye, A. Wy loma´ nska, Forecasting of symmetric�-stable autoregressive models by time series approach supported by artificial neural networks, Journal of Computational and Applied Mathematics 425 (2023) 115051.doi:10.1016/j.cam.2022.115051

  36. [44]

    L´ evy, Th´ eorie des erreurs

    P. L´ evy, Th´ eorie des erreurs. La loi de Gauss et les lois exceptionnelles, Bulletin de la Soci´ et´ e Math´ ematique de France 52 (1924) 49–85.doi:10.24033/bsmf.1046

  37. [45]

    Samorodnitsky, M

    G. Samorodnitsky, M. S. Taqqu, Stable non-Gaussian random processes: stochastic models with infinite variance, Vol. 1, CRC press, 1994

  38. [46]

    J. P. Nolan, Univariate Stable Distributions: Models for Heavy Tailed Data, Springer International Publishing, 2020. doi:10.1007/978-3-030-52915-4

  39. [47]

    Borak, W

    S. Borak, W. H¨ ardle, R. Weron, Stable distributions, Springer, 2005

  40. [48]

    P. J. Brockwell, R. A. Davis, Introduction to time series and forecasting, Springer, 2002

  41. [49]

    C. M. Gallagher, A method for fitting stable autoregressive models using the autocovariation function, Statistics & Probability Letters 53 (4) (2001) 381–390.doi:10.1016/s0167-7152(01)00041-4

  42. [50]

    Kruczek, A

    P. Kruczek, A. Wy loma´ nska, M. Teuerle, J. Gajda, The modified Yule-Walker method for�-stable time series models, Physica A: Statistical Mechanics and its Applications 469 (2017) 588–603.doi:10.1016/j.physa.2016.11.037

  43. [51]

    X. Ma, C. Nikias, Joint estimation of time delay and frequency delay in impulsive noise using fractional lower order statistics, IEEE Transactions on Signal Processing 44 (11) (1996) 2669–2687.doi:10.1109/78.542175

  44. [52]

    ˙Zu lawi´ nski, A

    W. ˙Zu lawi´ nski, A. Wy loma´ nska, Fractional lower-order covariance-based measures for cyclostationary time series with heavy-tailed distributions: Application to dependence testing and model order identification, Digital Signal Processing 163 (2025) 105214.doi:10.1016/j.ds...

  45. [53]

    G. Bontempi, Long term time series prediction with multi-input multi-output local learning, Proceedings of the 2nd European Symposium on Time Series Prediction (TSP), ESTSP08 (2008) 145–154

  46. [54]

    Ben Taieb, A

    S. Ben Taieb, A. Sorjamaa, G. Bontempi, Multiple-output modeling for multi-step-ahead time series forecasting, Neurocomputing 73 (10–12) (2010) 1950–1957.doi:10.1016/j.neucom.2009.11.030

  47. [55]

    Kidger, T

    P. Kidger, T. Lyons, Universal Approximation with Deep Narrow Networks, in: J. Abernethy, S. Agarwal (Eds.), Proceedings of Thirty Third Conference on Learning Theory, Vol. 125 of Proceedings of Machine Learning Research, PMLR, 2020, pp. 2306–2327. URLhttps://proceedings.mlr.p...

  48. [56]

    A. F. Agarap, Deep Learning using Rectified Linear Units (ReLU), preprint, arXiv:1803.08375 [cs.NE] (2018).doi: 10.48550/arXiv.1803.08375. 37

  49. [57]

    Borovykh, S

    A. Borovykh, S. Bohte, C. W. Oosterlee, Conditional time series forecasting with convolutional neural networks, preprint, arXiv:1703.04691 [stat.ML] (2017).doi:10.48550/ARXIV.1703.04691

  50. [58]

    Loshchilov, F

    I. Loshchilov, F. Hutter, Decoupled Weight Decay Regularization, in: International Conference on Learning Represen- tations (ICLR), 2019. URLhttps://openreview.net/forum?id=Bkg6RiCqY7

  51. [59]

    Chollet, et al., Keras: Deep Learning for humans,https://keras.io/, accessed in Google Colab environment, February–November 2025 (published in 2015)

    F. Chollet, et al., Keras: Deep Learning for humans,https://keras.io/, accessed in Google Colab environment, February–November 2025 (published in 2015)

  52. [60]

    Abadi, A

    M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Man´ e, R. Monga, S. Moore, D. Murray, C. Olah, M....

  53. [61]

    Casella, R

    G. Casella, R. L. Berger, Statistical Inference, 2nd Edition, Duxbury, 2002

  54. [62]

    Sarnaglia, V

    A. Sarnaglia, V. Reisen, C. L´ evy-Leduc, Robust estimation of periodic autoregressive processes in the presence of additive outliers, Journal of Multivariate Analysis 101 (9) (2010) 2168–2183.doi:10.1016/j.jmva.2010.05.006

  55. [63]

    C. C. Solci, V. Anselmo Reisen, A. J. Queiroz Sarnaglia, P. Bondon, Empirical study of robust estimation methods for PAR models with application to the air quality area, Communications in Statistics - Theory and Methods 49 (1) (2019) 152–168.doi:10.1080/03610926.2018.1533970

  56. [64]

    Gonzalez, J

    J. Gonzalez, J. Paredes, G. Arce, Zero-order statistics: A mathematical framework for the processing and char- acterization of very impulsive signals, IEEE Transactions on Signal Processing 54 (10) (2006) 3839–3851.doi: 10.1109/TSP.2006.880306. 38

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