Pith. sign in

REVIEW 3 major objections 7 minor 31 references

Semi-Periodic Activation for Time Series Classification

T0 review · 3 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that LeakySineLU, a semi-periodic activation whose derivative is periodic, is the best-ranked activation for time series classification on 112 equal-length UCR datasets in both an MLP and an FCN.

desk verdict A modest Snake variant whose FCN results are plausible but whose MLP pairwise counts sum to 127, not 112—so the central claim is currently not supported as written. read the letter →

arxiv 2412.09889 v1 pith:K3BFEHNC submitted 2024-12-13 cs.LG cs.AI

José Gilberto Barbosa de Medeiros Júnior , Andre Guarnier de Mitri , Diego Furtado Silva This is my paper · ORCID
classification cs.LGcs.AI
keywords LeakySineLUactivationfunctiontimeseriesclassificationsemi-periodicperiodicderivativedeeplearningUCRarchive
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 argues that activation functions for time series neural networks have been under-studied, and that the key desirable properties—unboundedness, non-linearity, monotonicity, and a periodic derivative—can be combined in one function. It proposes LeakySineLU, defined piecewise as $\sin^2(x)+x$ on positive inputs and half that on negative inputs, and evaluates it against nine standard activations on 112 equal-length UCR classification datasets. In both a simple MLP and a fully convolutional network, LeakySineLU achieves the best average rank among all compared activations, though its mean accuracy is often close to or slightly below ReLU and PReLU. The paper's central claim is that this semi-periodic activation is a stronger default choice for time series classification than commonly used alternatives.

What carries the argument

The central object is the LeakySineLU activation, $\sigma(x)=\sin^2(x)+x$ for $x>0$ and $\sigma(x)=(\sin^2(x)+x)/2$ otherwise. Its derivative is $\sigma'(x)=\sin(2x)+1$ on the positive side and $\sigma'(x)=(\sin(2x)+1)/2$ on the negative side, so the derivative is periodic with period $\pi$ on each side while the function itself is unbounded and monotonic. The discontinuity at $x=0$ is handled with a sub-derivative, exactly as ReLU and PReLU handle theirs. This combination—unbounded, monotonic, non-linear, and with a periodic derivative—is what the paper argues lets a network keep negative-valued observations (no dying ReLU) while still expressing periodic patterns.

What would settle it

Run the same MLP and FCN comparison with per-activation hyperparameter search and include the variable-length UCR datasets, reporting accuracy over multiple random seeds; if LeakySineLU no longer holds the best average rank in either architecture, or if its rank advantage over ReLU and PReLU falls within seed noise, the paper's central claim fails.

Watch

Extended reading notes

Core claim

On 112 equal-length datasets from the UCR archive, the paper reports that LeakySineLU achieves the best average rank among ten activation functions in both architectures tested: average rank 4.2402 in the MLP and 3.0491 in the FCN. In pairwise one-versus-one comparisons it wins a majority of datasets against ReLU, PReLU, and Snake, and in the FCN it is statistically different from ELU and Snake under the Wilcoxon test with Holm correction. Notably, in the MLP its mean accuracy (0.7081) is slightly below ReLU (0.7098) and PReLU (0.7097), so the rank advantage is not the same as a mean-accuracy advantage. The paper interprets the results as evidence that a semi-periodic activation captures periodic structure in time series better than monotonic or purely periodic alternatives, while avoiding the vanishing-gradient and dying-ReLU failure modes of bounded and zero-slope activations.

Load-bearing premise

The entire comparison uses one fixed training schedule (optimizer, learning rate, and epoch count) applied identically to every activation, and it covers only equal-length UCR datasets, so if that schedule happens to suit LeakySineLU or variable-length series are where periodic derivatives matter, the best-rank result will not generalize.

Editorial extensions

If this is right

  • LeakySineLU is a drop-in activation with no extra learnable parameters, applicable to both dense and convolutional time series classifiers.
  • On equal-length UCR classification, it ranks first among ten activations in both the MLP and FCN settings by average rank.
  • It preserves negative-valued observations instead of zeroing them, addressing the dying-ReLU information loss while remaining unbounded and non-linear.
  • Its derivative is periodic in both the positive and negative domains, giving networks a mechanism to represent periodic structure, consistent with the paper's Fourier-series motivation.
  • In pairwise comparisons, it wins a majority of datasets against ReLU, PReLU, and Snake in both architectures.

Reading between the lines

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

  • Because the evaluation used one fixed training schedule for every activation, a testable extension is to repeat the comparison with per-activation hyperparameter tuning and with variable-length UCR datasets included; the rank ordering could change under either condition.
  • The gap between LeakySineLU's best average rank and its slightly lower mean accuracy in the MLP suggests the rank advantage may come from avoiding worst-case failures on particular datasets, so the activation could be most valuable where other activations collapse.
  • The factor of 1/2 on the negative branch is an arbitrary design choice; testing other scales would reveal whether the exact constant matters for the reported advantage.
  • The same periodic-derivative principle could be tested on other time series tasks such as forecasting and extrinsic regression, where seasonal structure is even more central.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 7 minor

Summary. The paper proposes LeakySineLU, a piecewise activation defined as sigma(x) = sin^2(x) + x for x > 0 and (sin^2(x) + x)/2 otherwise, designed to combine unboundedness, non-linearity, monotonicity, and a semi-periodic derivative. It evaluates this activation against ReLU, PReLU, ELU, GeLU, SiLU, Snake, TanH, Sigmoid, and Sine in an MLP and an FCN on 112 equal-length UCR time-series classification datasets. The central claim is that LeakySineLU obtains the best average ranking in all comparative scenarios, supported by critical-difference diagrams, pairwise win/draw/loss counts, and multi-comparison matrices.

Significance. If the empirical claim holds, the paper offers a simple, drop-in activation that improves rank-based aggregate performance for time-series classification in simple MLP and FCN models, and its property checklist is a useful organizing device. The study is broad in benchmark coverage, uses standard non-parametric statistical post-hoc analysis, and makes the code available. There is no evident circularity: LeakySineLU is hand-designed from a property checklist and evaluated on external benchmarks, with no constants fitted to the UCR results. The significance is limited by the small mean-accuracy differences and the lack of repeated-seed variance; the contribution is incremental rather than a major advance, and the claims need to be calibrated to the actual evidence.

major comments (3)
  1. [§5, Figs. 4 and 6] The MLP pairwise win/draw/loss counts are inconsistent with the stated 112-dataset benchmark. In Fig. 4, LeakySineLU vs ReLU is 67/3/57 (sum 127), vs Snake is 75/3/49 (sum 127), and vs PReLU is 68/3/56 (sum 127); every comparison in the MLP MCM of Fig. 6 also sums to 127, whereas the FCN counts in Figs. 7 and 9 sum to 112. Since the captions and text state that the experiments use 112 equal-length datasets, the MLP ranking in Fig. 5 and the MCM in Fig. 6 may have been computed on a different dataset set. This is a load-bearing inconsistency for the abstract claim of best average ranking in all comparative scenarios. The authors must either rerun the MLP analysis on the declared 112 equal-length subset or re-scope the claim to the actual dataset set used; the FCN results alone cannot support the MLP claim.
  2. [§5, Figs. 6 and 9] The headline ranking result is accompanied by lower mean accuracy and mostly non-significant pairwise differences in both architectures. In the MLP MCM, LeakySineLU has mean accuracy 0.7081 against ReLU 0.7098 and PReLU 0.7097, with Wilcoxon p-values 0.8267 and 0.9042; in the FCN MCM it has 0.8006 against ReLU 0.8025, with p = 0.8583. No repeated-seed variance is reported, so the rank ordering may be unstable. The paper should either temper the claim from best to conditionally competitive, or add confidence intervals and repeated seeds and show that the average-rank advantage is not an artifact of a single run. A majority of wins in pairwise counts is not sufficient evidence of superiority when the mean accuracy is lower and the test is not significant.
  3. [§4.1, footnote 1] The benchmark is restricted to the 112 equal-length UCR datasets and to one fixed training schedule per architecture (Adadelta with lr = 1.0 and 1000 epochs for MLP; Adam with lr = 0.001 and 2000 epochs for FCN), while the abstract and conclusion speak of time series classification without this qualification. The paper should either narrow the claims to equal-length classification under this fixed protocol, or add evidence that the ranking is stable under variable-length series and different hyperparameters. Otherwise the generalization claim is not supported by the reported experiments.
minor comments (7)
  1. [§3.2, Eq. (5)] The otherwise branch of the definition is ambiguous; as printed, sin2(x)+x over 2 could be read as sin^2(x) + x/2 rather than (sin^2(x)+x)/2. Please add parentheses.
  2. [§3.3, Eqs. (7)-(8)] Because sin^2(+∞) is undefined, the limit argument should be written using inequalities, for example sin^2(x) ≥ 0, so x + sin^2(x) → +∞ and (x + sin^2(x))/2 → -∞.
  3. [§2, Definition 3] Definition 3 requires σ′(x+T) = σ′(x) for all x, but the derivative of LeakySineLU is undefined at x = 0 and the one-sided limits differ; please state that the periodicity holds on each branch of the derivative.
  4. [§3.5] The sub-derivative definition in Section 2 is stated for convex functions, but LeakySineLU is not convex; the treatment of the discontinuity at x = 0 should instead use one-sided derivatives or a clearly defined generalized subgradient.
  5. [§3.6] The Fourier-series motivation leading to Eq. (17) is not rigorous: cosine and sine basis terms with different arguments cannot be collapsed into a single sin(X) applied to an input matrix without specifying phase and argument-matching conditions.
  6. [§3.1] The sentence introducing the ReLU family is garbled: it says ReLU, ELU as an exponential linear unit (PReLU) for its learnable parameters, which conflates ELU and PReLU and introduces LeakyReLU elsewhere without defining it.
  7. [§5] The Friedman test is invoked following [5], but no Friedman statistic or p-value is reported; please include it or clarify that only the Wilcoxon signed-rank test with Holm correction is used.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: LeakySineLU is hand-designed and its headline claim is an empirical ranking on external UCR benchmarks, not a quantity re-derived from fitted inputs.

full rationale

The paper's central derivation is self-contained. LeakySineLU is explicitly defined in Eq. 5 as σ(x)=sin²(x)+x for x>0 and (sin²(x)+x)/2 otherwise, with derivative in Eq. 6. No parameter of the activation is fitted to UCR accuracies; the evaluation is a post-hoc comparison on an external benchmark suite. The theoretical motivation in Sec. 3.6 uses Fourier-series reasoning to justify periodic or semi-periodic activation, but it does not derive the benchmark result from the activation formula. The only arguable overlap is that the positive branch equals Snake with a=1 (Snake formula in Table 1 is x+sin²(ax)/a), which is a design similarity, not a circular dependency: the paper does not claim to derive Snake or to predict its ranking from its own formula. There are no load-bearing self-citations: references [31], [5], and [10] are external prior work. The reported win/loss/draw counts that sum to 127 rather than 112 for MLP (Figs. 4 and 6) are an internal consistency concern about the empirical claim, not a circularity of the derivation chain.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central empirical claim rests on the hand-designed activation shape, the equal-length UCR subset, and the fixed training protocol. There are no fitted constants in the activation itself, but the three design choices listed above are free parameters in the sense of being chosen by hand rather than learned or derived.

free parameters (3)
  • Negative branch scale factor = 1/2
    LeakySineLU divides the positive-branch expression by 2 for x<0. This factor is chosen by hand to keep the derivative in [0,0.5] on the negative branch; it is not learned and no search is reported.
  • Snake period parameter a = 1
    The positive branch sin^2(x)+x equals Snake with a=1. The paper fixes this parameter implicitly and does not tune or discuss it.
  • Branch threshold = 0
    The switch between the two branches is at x=0, chosen for convenience and standard practice; no data-driven selection is reported.
assumptions (3)
  • domain assumption Universal Extrapolation Theorem from [31] is valid and implies usefulness of periodic components
    Section 3.6 uses this theorem to motivate periodic activations but does not prove it, and the connection from the Fourier-series rewrite to the specific form sin^2(x)+x is not established.
  • domain assumption The 112 equal-length UCR datasets are a representative benchmark for time series classification
    The paper draws general conclusions about time series classification from this subset, while footnote 1 explicitly excludes variable-length datasets.
  • standard math Sub-gradient optimization at the x=0 kink behaves like ReLU and PReLU
    Section 3.5 defines the sub-derivative set {0.5, 1} at 0 and relies on standard practice for non-differentiable activations, but no convergence analysis is given for this specific function.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Semi-Periodic Activation for Time Series Classification." pith.science (2026). https://pith.science/paper/K3BFEHNC

@misc{pith2026241209889,
  author       = {Pith},
  title        = {Pith review of: Semi-Periodic Activation for Time Series Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K3BFEHNC}},
  note         = {Machine review of arXiv:2412.09889}
}
read the original abstract

This paper investigates the lack of research on activation functions for neural network models in time series tasks. It highlights the need to identify essential properties of these activations to improve their effectiveness in specific domains. To this end, the study comprehensively analyzes properties, such as bounded, monotonic, nonlinearity, and periodicity, for activation in time series neural networks. We propose a new activation that maximizes the coverage of these properties, called LeakySineLU. We empirically evaluate the LeakySineLU against commonly used activations in the literature using 112 benchmark datasets for time series classification, obtaining the best average ranking in all comparative scenarios.

Figures

Figures reproduced from arXiv: 2412.09889 by the authors.

Figure 1
Figure 1. shows the results of activations used in neural networks for time series. ReLU (a) is the most used choice as an activation function in these networks. In the example observed in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. presents the image domain and the derivative of LeakySineLU. It’s possible to note that the derivate is not continuous when x = 0. As in ReLU and PReLU activations, this problem is solved using sub-derivatives and sub-gradients (c.f. Section 3.5). However, we maintain a periodic behavior on the derivative in both domains, negative and positive, and an increased non-linearity by the difference on the scale for these … view at source ↗
Figure 3
Figure 3. Effects of boundaries on time series feature maps. Red region presents the information lost, which means that f(x) = 0 for that values [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Comparison between LeakySineLU, ReLU, PReLU, and Snake on an MLP network among the 112 equal-length datasets [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: presents the Critical Difference diagram generated over the experiments in the UCR repository with the MLP network. Note that the LeakySineLU function is in the 1 st position with an average ranking of 4.2402, besides not having a statistical difference from Snake and …
Figure 6
Figure 6. Figure 6: Multi-Comparison Matrix of LeakySineLU in an MLP compared to the other statistical equivalent approaches in [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Comparison between LeakySineLU, ReLU, PReLU, and Snake on an FCN network among the 112 equal-length datasets [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: presents the Critical Difference diagram generated over the experiments in the UCR repository with the FCN network. In this case, the critical difference diagram shows that LeakySineLU is statistically different from ELU. However, it is worth remembering that, as highl…
Figure 9
Figure 9. Figure 9: Multi-Comparison Matrix of LeakySineLU in a FCN compared to the other statistical equivalent approaches in [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

31 extracted references · 13 canonical work pages

  1. [1]

    IEEE Transactions on Neural Networks5(2), 157–166 (1994)

    Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks5(2), 157–166 (1994). https://doi.org/10.1109/72.279181

    doi:10.1109/72.279181 1994

  2. [2]

    In: Bengio, Y., LeCun, Y

    Clevert, D., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2- 4, 2016, Conference Track Proceedings (2016),http://arxiv.org/abs/1511.07289

    arXiv 2016

  3. [3]

    Dau, H.A., Keogh, E., Kamgar, K., Yeh, C.C.M., Zhu, Y., Gharghabi, S., Ratanama- hatana, C.A., Yanping, Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G., Hexagon-ML: The ucr time series classification archive (October 2018), https://www.cs.ucr.edu/~eamonn/time_series_data_2018/

    work page 2018

  4. [4]

    CoRRabs/1702.03118 (2017), http://arxiv.org/abs/1702.03118

    Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. CoRRabs/1702.03118 (2017), http://arxiv.org/abs/1702.03118

    arXiv 2017

  5. [5]

    Data Mining and Knowledge Discovery33(4), 917–963 (mar 2019).https://doi.org/10.1007/s10618-019-00619-1, https:// doi.org/10.1007%2Fs10618-019-00619-1

    Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. Data Mining and Knowledge Discovery33(4), 917–963 (mar 2019).https://doi.org/10.1007/s10618-019-00619-1, https:// doi.org/10.1007%2Fs10618-019-00619-1

    doi:10.1007/s10618-019-00619-1 2019

  6. [6]

    Data Mining and Knowledge Discovery34(6), 1936–1962 (sep 2020)

    Fawaz, H.I., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D.F., Weber, J., Webb, G.I., Idoumghar, L., Muller, P.A., Petitjean, F.: InceptionTime: Finding AlexNet for time series classification. Data Mining and Knowledge Discovery34(6), 1936–1962 (sep 2020). https://doi.org/10.1007/s10618-020-00710-y, https: //doi.org/10.1007%2Fs10618-020-00710-y

    doi:10.1007/s10618-020-00710-y 1936

  7. [7]

    Foumani, N.M., Miller, L., Tan, C.W., Webb, G.I., Forestier, G., Salehi, M.: Deep learning for time series classification and extrinsic regression: A current survey (2023)

    work page 2023

  8. [8]

    In: 2015 IEEE International Conference on Computer Vision (ICCV)

    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human- level performance on imagenet classification. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 1026–1034 (2015).https://doi.org/10.1109/ ICCV.2015.123

    work page 2015

Show all 31 references
  1. [9]

    CoRRabs/1606.08415 (2016), http://arxiv.org/ abs/1606.08415

    Hendrycks, D., Gimpel, K.: Bridging nonlinearities and stochastic regularizers with gaussian error linear units. CoRRabs/1606.08415 (2016), http://arxiv.org/ abs/1606.08415

    2016 arXiv

  2. [10]

    arXiv preprint arXiv:2305.11921 (2023)

    Ismail-Fawaz, A., Dempster, A., Tan, C.W., Herrmann, M., Miller, L., Schmidt, D.F., Berretti, S., Weber, J., Devanne, M., Forestier, G., et al.: An approach to multiple comparison benchmark evaluations that is stable under manipulation of the comparate set. arXiv preprint arXi...

    2023 arXiv

  3. [11]

    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2017)

    2017

  4. [12]

    Neurocomputing 216, 718–734 (2016)

    Liew, S.S., Khalil-Hani, M., Bakhteri, R.: Bounded activation functions for en- hanced training stability of deep neural networks on visual pattern recogni- tion problems. Neurocomputing 216, 718–734 (2016). https://doi.org/https: //doi.org/10.1016/j.neucom.2016.08.037, https:...

    2016 doi

  5. [13]

    Big Data Research34, 100407 (2023).https://doi.org/https://doi.org/10

    Lima, F.T., Souza, V.M.: A large comparison of normalization methods on time se- ries. Big Data Research34, 100407 (2023).https://doi.org/https://doi.org/10. 1016/j.bdr.2023.100407, https://www.sciencedirect.com/science/article/ pii/S2214579623000400

    2023

  6. [14]

    Misra, D.: Mish: A self regularized non-monotonic activation function (2020) Semi-Periodic Activation for Time Series Classification 15

    2020

  7. [15]

    In: Proceedings of the 27th international conference on machine learning (ICML-10)

    Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10). pp. 807–814 (2010)

    2010

  8. [16]

    Parascandolo, G., Huttunen, H., Virtanen, T.: Taming the waves: sine as activation function in deep neural networks (2017), https://openreview.net/forum?id= Sks3zF9eg

    2017

  9. [17]

    Pedamonti, D.: Comparison of non-linear activation functions for deep neural networks on mnist classification task (2018)

    2018

  10. [18]

    Acta Numerica 8, 143–195 (1999).https://doi.org/10.1017/S0962492900002919

    Pinkus, A.: Approximation theory of the mlp model in neural networks. Acta Numerica 8, 143–195 (1999).https://doi.org/10.1017/S0962492900002919

    1999 doi

  11. [19]

    In: 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Rasamoelina, A.D., Adjailia, F., Sinčák, P.: A review of activation function for artificial neural network. In: 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI). pp. 281–286 (2020).https://doi.org/10. 1109/SAMI48414.2020.9108717

    2020

  12. [20]

    Data Mining and Knowledge Discovery35, 401–449 (2021)

    Ruiz, A.P., Flynn, M., Large, J., Middlehurst, M., Bagnall, A.: The great multi- variate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery35, 401–449 (2021). https://doi.org/https://doi...

    2021 doi

  13. [21]

    In: IJCNN’99

    Silvescu, A.: Fourier neural networks. In: IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339). vol. 1, pp. 488–491 vol.1 (1999). https://doi.org/10.1109/IJCNN.1999.831544

    1999

  14. [22]

    Applied and Computational Harmonic Analysis43(2), 233– 268 (2017)

    Sonoda, S., Murata, N.: Neural network with unbounded activation functions is universal approximator. Applied and Computational Harmonic Analysis43(2), 233– 268 (2017). https://doi.org/https://doi.org/10.1016/j.acha.2015.12.005, https://www.sciencedirect.com/science/article/pi...

    2017 doi

  15. [23]

    In: 1999 Ninth International Conference on Artificial Neural Networks ICANN 99

    Sopena, J., Romero, E., Alquezar, R.: Neural networks with periodic and monotonic activation functions: a comparative study in classification problems. In: 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470). vol. 1, pp. 323–328 vo...

    1999 doi

  16. [24]

    Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: A strong baseline (2016)

    2016

  17. [25]

    In: Proceedings of the Thirtieth Interna- tional Joint Conference on Artificial Intelligence

    Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., Xu, H.: Time series data augmentation for deep learning: A survey. In: Proceedings of the Thirtieth Interna- tional Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence ...

    2021 doi

  18. [26]

    Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., Sun, L.: Transformers in time series: A survey (2023)

    2023

  19. [27]

    In: 2019 IEEE International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE)

    Wu, J., Zhang, Z., Ji, Y., Li, S., Lin, L.: A resnet with ga-based structure optimiza- tion for robust time series classification. In: 2019 IEEE International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE). pp. 69–74 (2019). https://doi.org/10.11...

    2019

  20. [28]

    Knowledge-Based Systems 245, 108606 (2022)

    Yang, X., Zhang, Z., Cui, R.: Timeclr: A self-supervised contrastive learn- ing framework for univariate time series representation. Knowledge-Based Systems 245, 108606 (2022). https://doi.org/https://doi.org/10.1016/j. knosys.2022.108606, https://www.sciencedirect.com/science...

    2022

  21. [29]

    Zeiler, M.D.: Adadelta: An adaptive learning rate method (2012)

    2012

  22. [30]

    Zhumekenov, A., Uteuliyeva, M., Kabdolov, O., Takhanov, R., Assylbekov, Z., Castro, A.J.: Fourier neural networks: A comparative study (2019) 16 J. G. B. de Medeiros Júnior et al

    2019

  23. [31]

    In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H

    Ziyin, L., Hartwig, T., Ueda, M.: Neural networks fail to learn periodic functions and how to fix it. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 1583–1594. Curran Associates, Inc. (20...

    2020

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.