Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach

Danny H.K. Tsang; Jingwei Li; Lei Deng; Wenhan Xu

REVIEW 4 major objections 5 minor 51 references

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Forecasting network traffic from incomplete history can run in under 100 ms with MAE below 0.002 by completing a low-tubal-rank traffic tensor with a pre-trained generative model and a learned latent-space optimizer.

desk verdict Sensible engineering combination for traffic forecasting with missing data, with a plausible empirical result, but the stated recovery guarantee does not cover the deployed algorithm and its proof is omitted. read the letter →

arxiv 2506.09647 v1 pith:ERFHQEDA submitted 2025-06-11 cs.NI cs.LG

Lei Deng , Wenhan Xu , Jingwei Li , Danny H.K. Tsang This is my paper · ORCID

classification cs.NIcs.LG

keywords networktrafficforecastingtensorcompletiongenerativemodelst-SVDlow-tubal-ranktensorsmissingdatareal-timeinferencelearnedoptimizer

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

Network traffic forecasting usually assumes the historical traffic matrices are fully observed, but real deployments lose entries to monitor failures, sampling choices, and transmission problems. This paper argues that forecasting under missing data can be treated as tensor completion and solved in real time by a pre-trained generative model: one network learns to map a compact latent code — the singular tubes of the traffic tensor — back to a complete low-tubal-rank tensor, while a second tiny network learns to update that code from the observed entries. The forecast is then simply the future slice of the completed tensor, with no separate imputation step, and the paper reports inference below 100 ms with mean absolute error under 0.002 on Abilene and similar accuracy on GÉANT across missing rates from 10% to 90%. The paper also proves an error bound that extends the generative compressed sensing guarantee from vectors to tensors. If the claims hold, operators could predict congestion from incomplete measurement data quickly enough to act on it, where impute-then-forecast pipelines exceed the time budget.

What carries the argument

The load-bearing object is the pre-trained generator $G_\theta$ together with the t-SVD representation it inverts. A traffic record is a third-order tensor $T \in \mathbb{R}^{n\times n\times T}$ (source × destination × time); its t-SVD $T = U * S * V^\top$ is taken under the t-product, where the tubes along the time axis are combined by circular convolution, and the tubal rank is the number of nonzero singular tubes. The vector $v$ formed by stacking those singular tubes is the latent code, so the generator is trained to satisfy $G_\theta(v) \approx T$, using tensor layers whose channel widths enforce low tubal rank. Inference minimizes $\lVert M - P_\Omega(G_\theta(z))\rVert_F^2 + \gamma\,\lVert G_\theta(z)\rVert_{\mathrm{TNN}}$ over $z$, with the tensor nuclear norm replaced in practice by the $\ell^1$ norm of the spectral latent code; the minimizer is found by a small learned network $Q_\phi$ that updates $z$ from the observed tensor and the current code in a fixed number of steps. The formal result, Lemma B.3, bounds $\lVert G_\theta(\hat{z}) - T\rVert_F$ for gradient descent on this objective, and it is the extension to tensors of the generative compressed sensing guarantee.

What would settle it

On held-out Abilene and GÉANT tensors, compute the objective value in Eq. (5) at the learned optimizer's output $\hat z$ after $K$ steps and compare it with the value at the best $z$ found by many random-restart gradient descent iterations; if that gap exceeds the $\varepsilon$ slack of Lemma B.3, or if MAE on structured block-missing masks rather than uniform random masks exceeds 0.002, the deployed real-time accuracy claim is refuted.

Watch

Extended reading notes

Core claim

On the paper's own terms, the core claim is that a pre-trained generative model can supply the low-rank structure needed for tensor completion, and that optimizing in latent space makes traffic forecasting fast enough for real-time operation on incomplete data. The generator $G_\theta$ is trained so that the vector $v$ of singular tubes obtained from the t-SVD of complete traffic tensors maps back to the tensor itself; at inference, an unseen partially observed tensor $M$ is completed by iterating a learned optimizer $Q_\phi$ on the latent code $z$ to minimize $\lVert M - P_\Omega(G_\theta(z))\rVert_F^2 + \gamma\,\lVert G_\theta(z)\rVert_{\mathrm{TNN}}$, and the future slices of the completed tensor are the forecast. The paper reports that this achieves MAE below 0.002 within 100 ms on the Abilene dataset for missing rates 0.1 to 0.9, outperforming or matching imputation-based deep baselines. The supporting theory, Lemma B.3, bounds the reconstruction error by $6\min_{z_*}\lVert G_\theta(z_*) - T\rVert_F + 2\varepsilon$ with probability $1 - e^{-\Omega(|\Omega|)}$ when the generator is a ReLU network and the observed entries are sampled uniformly at random — the tensor analogue of the compressed sensing guarantee for generative models.

Load-bearing premise

The paper assumes that the small learned optimizer $Q_\phi$, trained on complete tensors and their true singular vectors, will still produce a near-minimizing latent code for unseen incomplete tensors within a fixed, small number of update steps; the formal error bound applies only to gradient descent on the objective, not to this learned optimizer.

Editorial extensions

If this is right

Forecasting becomes tensor completion: given a partially observed history, the predicted matrices are just the future slices of the completed tensor, so the separate imputation-then-forecast pipeline is eliminated.
Because the optimization runs on the latent code of dimension $r T$ (tubal rank times time length) rather than on the full tensor, inference cost drops into the sub-100 ms range on the tested Abilene (12 nodes) and GÉANT (23 nodes) networks.
The tensor-layer variant GMF_TL beats its fully connected counterpart, indicating that t-product-based layers preserve the low-tubal-rank prior that plain layers lose.
The recovery guarantee says that with uniform random sampling and enough observations, the completed tensor is within a constant factor of the best reconstruction the generator can produce, plus an optimization slack — a compressed sensing-style assurance.
Accuracy stays nearly flat as missingness grows from 10% to 90% (MAE about 0.0014–0.0016 on Abilene), while imputation-based baselines degrade, so the method's advantage grows in data-sparse regimes.

Reading between the lines

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

A stress test the paper does not run is structured missingness: real failures such as a dead monitor or a congested link erase blocks or whole slices, whereas Lemma B.3's sampling model assumes uniform random support, so the guarantee may not transfer to those regimes.
The learned optimizer trades the formal guarantee for speed: Lemma B.3 covers gradient descent, not the deployed $Q_\phi$ in Algorithm 4, so the real-time accuracy claim rests on an empirical generalization assumption that could be checked by measuring the gap between $Q_\phi$'s output and the best random-restart gradient descent solution on held-out tensors.
The same latent-optimization scheme is portable to other tensor-valued network telemetry, such as latency or packet-loss estimation, whenever a low-tubal-rank prior and a pre-trained generator are available.
The sample-complexity scaling in Lemma B.3 (observations proportional to $r T l \log(n^2 T)$) predicts a performance cliff as node count or horizon grows, which experiments on larger backbone networks could expose.

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.

Desk Editor's Note

read the letter

Quick take: GMF is a sensible engineering combination that gets under 100ms forecasting with MAE around 0.0015 on Abilene and GÉANT, and the tensor-layer variant clearly beats its own FC variant. The empirical part is the value here. What's actually new is modest: CSGM (Bora et al.), tensor layers (Liu et al.), and learned optimizers (Andrychowicz et al.) are all known; the paper's contribution is putting them together for network traffic forecasting with missing data, plus a tensor-layer generator that maps latent codes to traffic tensors. That's a real contribution, but incremental.

What it does well: the problem is practical and the evaluation is straightforward. The ablation of tensor layers vs FC layers supports their architecture choice. The runtime comparison, though coarse, shows the intended advantage. Appendix A's CDF of singular tube energy for DCGAN/PGAN is a nice sanity check.

Soft spots: the theoretical guarantee is not delivered. Lemma B.3's proof is omitted with “can be derived by extending Theorem 1.1 in [6]”. More importantly, the bound is for an additive epsilon-minimizer of (5) under Gaussian measurements, while GMF (Alg. 4) uses the learned optimizer Q_phi with binary entry masks. Nothing shows that Q_phi's output is an epsilon-minimizer on unseen tensors, and binary masks don't satisfy the Gaussian variance condition in Def. B.2. So the abstract's “theoretical recovery guarantee” covers neither the measurement operator nor the inference procedure actually deployed. That should be fixed by either proving the needed property for Q_phi, or clearly labeling the theory as applying to the unaccelerated framework only.

Also, no code, no error bars, no normalization details. The computational table gives ranges, not exact times or standard deviations. For a claim of real-time performance, that's thinner than it should be.

On balance: the empirical claim is plausible and the method is reasonable, but the paper overstates its theoretical support. It needs a major revision, not a desk reject. My recommendation: send it to review, but make clear that the theory must be reconciled with the algorithm, and the experiments need reproducibility. If I needed a citation for “generative-model tensor completion for traffic forecasting,” I'd probably cite it despite the gaps, because there isn't much else.

Referee Report

4 major / 5 minor

Summary. The paper proposes GMF, a generative-model approach for real-time network traffic forecasting under missing data. The forecasting task is formulated as a low-tubal-rank tensor completion problem, and a pre-trained generator maps a low-dimensional latent code to a complete traffic tensor. Inference is accelerated by a learned optimizer that updates the latent code directly. The authors claim a theoretical recovery guarantee, supported by Appendix B, and report on real-world datasets (Abilene, GEANT) that GMF achieves MAE below 0.002 and inference under 100 ms.

Significance. If the claims are fully supported, GMF would be a useful contribution: it is one of the few works addressing missing-data traffic forecasting by tensor completion with generative priors, and the tensor-layer generator combined with a learned latent-space optimizer is a plausible way to achieve real-time inference. The paper explicitly includes an ablation of tensor layers versus fully connected layers, evaluates on two real-world datasets, and provides a quantitative real-time comparison. However, the significance is substantially tempered because the theoretical recovery guarantee is not proved and, as stated, does not cover the actual measurement operator or the actual inference algorithm used in the experiments. The empirical results are promising but need stronger statistical reporting.

major comments (4)

[Appendix B, Lemmas B.1 and B.3] The only formal support for the claimed recovery guarantee is not proved. Lemma B.1 is justified only by "The proof ... can be derived by extending Theorem 2 in [10]", and Lemma B.3 by "We omit the proof here, which can be derived by extending Theorem 1.1 in [6] from the vector to the tensor space". Moreover, Lemma B.3 omits any Lipschitz bound or weight-norm control on G_theta, which is essential in Bora et al.'s Theorem 1.1; as written, the sample complexity O(k n3 l log(n^2 n3)) is not derived from the stated assumptions. Because the abstract advertises a theoretical recovery guarantee, this is a load-bearing gap rather than a minor omission.
[Section 5.1.3, Eq. (8); Algorithm 4] The deployed inference algorithm does not satisfy the hypothesis of Lemma B.3. The lemma requires z_hat to be an additive epsilon-minimizer of the objective in Eq. (5), but Algorithm 4 outputs z_{K+1} produced by K applications of the learned optimizer Q_phi (Eq. (8)). Q_phi is trained on the supervised loss in Eq. (9) using the ground-truth singular vector v and full tensor T. No analysis demonstrates that for unseen M, the resulting z_{K+1} is an epsilon-minimizer of Eq. (5). The real-time accuracy claim therefore rests on an unproved generalization assumption about Q_phi.
[Definition B.2 and Section 6 (experimental setup)] The measurement model assumed in the theory is not the one used in the experiments. Definition B.2 requires the measurement matrix A to have i.i.d. Gaussian entries, whereas the experiments generate M = P_Omega(T) by binary coordinate masks applied only to the historical slices (Section 6). Uniform random coordinate masks do not satisfy the Gaussian variance condition, and the mask does not cover the future slice. Consequently, even if Lemma B.3 were proved, it could not directly explain the numerical results in Table 1.
[Section 4, Eq. (5); Appendix B, Lemma B.3] Lemma B.3 states that "there exists gamma >= 0" such that the bound holds for a near-minimizer of Eq. (5). This statement is ambiguous: if gamma is chosen to be 0, the TNN regularization is immaterial and the result reduces to the standard CSGM bound, whereas if gamma > 0, the extra regularizer changes the minimizer and is not analyzed in the proof sketch. The text does not explain how gamma is set in the optimization used in Eq. (6) or in Algorithm 2, making the formal statement incomplete.

minor comments (5)

[Figure 2 caption] The caption contains "genitive models" and should read "generative models".
[Table 1] The table reports single MAE and NRMSE values without standard deviations or confidence intervals; the authors should report means and variances over random masks and initialization seeds.
[Table 2] The real-time comparison uses only qualitative bands ("<100 ms", ">2 s"); report actual inference times, for example mean and 99th percentile over the test set.
[Section 5.2, Remark] The complexity comparison contains typographical errors in the exponents (e.g., "O(n2n3 + n3n3)"); please correct the formula and define the underlying t-SVD and t-product complexities it is based on.
[Algorithms and equations] Algorithm 1 and several equations contain unresolved LaTeX control sequences such as "/u1D45B1" and "/u1D45D5"; ensure the final manuscript typesets correctly.

Circularity Check

1 steps flagged · score 3.0 of 10

The empirical forecasting claim is held-out and not circular, but the theoretical recovery guarantee relies on a self-cited lemma (Lemma B.1) and an acknowledged restatement of Bora et al.'s theorem.

self citation load bearing [Appendix B, Lemma B.1]
"The proof of Lemma B.1 can be derived by extending Theorem 2 in [10] to the t-product case."

Lemma B.1 is the load-bearing bridge between the actual observation model M = P_Omega(T), which uses coordinate masks, and the vectorized sparse form m_j = A g_j that Definition B.2 and Lemma B.3 require. Rather than proving this lemma, the paper reduces it to the authors' own prior work [10], with no derivation supplied. The theoretical recovery guarantee therefore rests, at this critical step, on an unverified self-citation instead of a self-contained argument. This does not make the empirical forecasting claim circular, because the experiments use a temporal 80/20 train/test split and the future slice is not revealed to the model, but it makes the advertised theory dependent on the authors' prior result.

full rationale

The central empirical claim is not circular. The generator G_theta and the learned optimizer Q_phi are trained on the first 80% of the traffic matrices and evaluated on the held-out last 20%; the future slice is not included in the observed tensor M at inference, and missing data are simulated only on the historical portion. The reported MAE and NRMSE values are comparisons on held-out test periods, so the accuracy claim is externally validated rather than forced by construction. The main circularity-type concern is confined to the theoretical appendix: Lemma B.1 is justified solely by a self-citation to the authors' own [10], and Lemma B.3 is explicitly said to be derivable by extending Theorem 1.1 in [6] with its proof omitted. These are derivation and support weaknesses rather than a reduction of the empirical result to its inputs. Accordingly, the score reflects one load-bearing self-citation in the theory, while the central forecasting result retains independent content.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. The free parameters are standard hyperparameters, but their values are largely unreported. The key axioms are that traffic tensors are near a low-tubal-rank generative-model family, that the implemented sparsity proxy approximates the tensor nuclear norm, and that the Gaussian measurement model is a valid stand-in for entrywise masking. The first is plausible but unverified on the test split; the second and third are ad hoc and not justified.

free parameters (7)

gamma (TNN regularization weight in Eq. 5)
Hand-chosen hyperparameter controlling the low-rank penalty; no selection procedure or sensitivity analysis reported.
gamma0 (pre-training TNN weight in Alg. 2)
Hand-chosen; value not specified in the experiments.
alpha, beta (learner loss weights in Eq. 9)
Balancing weights for latent-vector error and reconstruction error; values not reported.
rho (learning rate in Alg. 3) = 0.01
Fixed value stated in Alg. 3; no schedule or tuning described.
K (number of learned optimizer steps)
Number of latent update steps in Alg. 3 and Alg. 4; not specified in the experiments.
Latent dimension n*n_3 = 132 (Abilene), 253 (GÉANT)
Chosen as the number of singular tubes, a modeling choice; not derived from data or theory.
Generator architecture (tensor layer width and depth)
The generator is described as sigma(TL(sigma(FC(...)))), but exact layer counts, hidden sizes, and activation functions are not given.

assumptions (4)

domain assumption Network traffic tensors are low-tubal-rank and lie near the range of a pretrained generator.
Invocated in Sections 3.2 and 4, motivated by refs [10,44] but not verified on the test distribution; if traffic patterns shift temporally, the generator may not represent them.
ad hoc to paper ||G_theta(z)||_TNN can be approximated by ||z~||_1, where z is the latent vector.
Section 4 after Eq. (5); no proof that the singular tubes of G_theta(z) are close to z; the implemented regularizer differs from the declared TNN.
ad hoc to paper The measurement operator can be modeled as m_j = A g_j with Gaussian entries.
Appendix B, Definition B.2; actual observation is entrywise masking P_Omega, not Gaussian linear measurements; the reduction in Lemma B.1 is asserted without proof.
domain assumption Lemma B.3 follows by extending Theorem 1.1 of [6] and Lemma B.1 extends Theorem 2 of [10].
Appendix B; proofs are omitted, so the theoretical guarantee is not established within this paper.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach." pith.science (2026). https://pith.science/paper/ERFHQEDA

@misc{pith2026250609647,
  author       = {Pith},
  title        = {Pith review of: Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ERFHQEDA}},
  note         = {Machine review of arXiv:2506.09647}
}

read the original abstract

Real-time network traffic forecasting is crucial for network management and early resource allocation. Existing network traffic forecasting approaches operate under the assumption that the network traffic data is fully observed. However, in practical scenarios, the collected data are often incomplete due to various human and natural factors. In this paper, we propose a generative model approach for real-time network traffic forecasting with missing data. Firstly, we model the network traffic forecasting task as a tensor completion problem. Secondly, we incorporate a pre-trained generative model to achieve the low-rank structure commonly associated with tensor completion. The generative model effectively captures the intrinsic low-rank structure of network traffic data during pre-training and enables the mapping from a compact latent representation to the tensor space. Thirdly, rather than directly optimizing the high-dimensional tensor, we optimize its latent representation, which simplifies the optimization process and enables real-time forecasting. We also establish a theoretical recovery guarantee that quantifies the error bound of the proposed approach. Experiments on real-world datasets demonstrate that our approach achieves accurate network traffic forecasting within 100 ms, with a mean absolute error (MAE) below 0.002, as validated on the Abilene dataset.

Figures

Figures reproduced from arXiv: 2506.09647 by the authors.

**Figure 2.** Illustration of the low-tubal-rank TC framework based on genitive models. R 1×2×3 is then used to map this latent representation zˆ to its corresponding tensor T, such that: T = (zˆ). (2) We now provide an intuitive explanation of how Equation (2) operates. Let T = U ∗ S ∗ V⊤ be the t-SVD of T and define the singular vector v = [S (1, 1, :), · · · ,S (, , :)]. This vector v is constructed by stacking all the singula… view at source ↗

**Figure 3.** The framework of the GMF scheme 5 Generative Models for Real-Time Network Traffic Forecasting with Missing Data We now elaborate the technical details of our GMF scheme for real-time network traffic forecasting with missing data, which comprises two key phases: a pre-training phase and an online inference phase. 5.1 Phase-1: Pre-training of the Generative Model As presented in Section 4, a pre-trained generative mod… view at source ↗

**Figure 4.** Energy CDFs of singular tube of tensors generated by g [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 45 canonical work pages

[6]

Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis. 201 7. Compressed sensing using generative models. In International conference on machine learning . PMLR, 537–546

work page
[10]

Lei Deng, Xiao-Yang Liu, Haifeng Zheng, Xinxin Feng, and Zhizhang Chen. 2 023. Graph-Tensor Neural Networks for Network Traﬃc Data Imputation. IEEE/ACM Transactions on Networking(2023), 1–15. doi:10.1109/TNET.2023.3268982

work page arXiv 2023
[1]

Imad Alawe, Adlen Ksentini, Yassine Hadjadj-Aoul, and Philippe Be rtin. 2018. Improving traﬃc forecasting for 5G core network scalability: A machine learning approach. IEEE Network 32, 6 (2018), 42–49

work page 2018
[2]

Davide Andreoletti, Sebastian Troia, Francesco Musumeci, Silvia Giordano, Guido Maier, and Massimo Tornatore

work page
[3]

Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Mat thew W Hoﬀman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas. 2016. Learning to learn by g radient descent by gradient descent. In Proceedings of the 30th International Conference on Neural Information Processing Systems. 3988–3996

work page 2016
[4]

Ons Aouedi, Van An Le, Kandaraj Piamrat, and Yusheng Ji. 2025. Dee p learning on network traﬃc prediction: Recent advances, analysis, and future directions. Comput. Surveys 57, 6 (2025), 1–37

work page 2025
[5]

Piotr Bojanowski, Armand Joulin, David Lopez-Pas, and Arthur Szl am. 2018. Optimizing the Latent Space of Genera- tive Networks. In International Conference on Machine Learning . PMLR, 600–609

work page 2018
[7]

Maolin Che, Xuezhong Wang, Yimin Wei, and Xile Zhao. 2022. Fast rand omized tensor singular value thresholding for low-rank tensor optimization. Numerical Linear Algebra with Applications 29, 6 (2022), e2444

work page 2022

Show all 51 references

[8]

Lei Deng, Xiao-Yang Liu, and Danny H. K. Tsang. 2025. Real-Time Netw ork Latency Estimation with Pretrained Generative Models. IEEE Transactions on Neural Networks and Learning Systems (2025)

2025
[9]

Lei Deng, Xiao-Yang Liu, Haifeng Zheng, Xinxin Feng, and Youjia Chen. 202 2. Graph Spectral Regularized Tensor Completion for Traﬃc Data Imputation. IEEE Transactions on Intelligent Transportation Systems 23, 8 (2022), 10996– 11010. doi:10.1109/TITS.2021.3098637

2022
[11]

Lei Deng, Xiao-Yang Liu, Haifeng Zheng, Xinxin Feng, Ming Zhu, and Danny H. K. Tsang. 2025. Graph-Tensor FISTA- Net: Edge Computing-Aided Deep Learning for Distributed Traﬃc Data R ecovery. IEEE Transactions on Network Science and Engineering (2025), 1–13. doi:10.1109/TNSE.202...

2025
[12]

Lei Deng, Haifeng Zheng, Xiao-Yang Liu, Xinxin Feng, and Zhizhang David C hen. 2020. Network Latency Estimation With Leverage Sampling for Personal Devices: An Adaptive Tensor Com pletion Approach. IEEE/ACM Transactions on Networking 28, 6 (2020), 2797–2808. doi:10.1109/TNET.2...

2020
[13]

Yin Gao, Man Zhang, Jiajun Chen, Jiren Han, Dapeng Li, and Ruitao Qiu. 2021. Accurate load prediction algorithms assisted with machine learning for network traﬃc. In2021 international wireless communications and mobile computing (IWCMC). IEEE, 1683–1688

2021
[14]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, D avid Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems 27 (2014)

2014
[15]

Qing He, Arash Moayyedi, György Dán, Georgios P Koudouridis, and Per Tengkvist. 2020. A meta-learning scheme for adaptive short-term network traﬃc prediction. IEEE Journal on Selected Areas in Communications 38, 10 (2020), 2271–2283

2020
[16]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-t erm memory. Neural computation 9, 8 (1997), 1735–1780

1997
[17]

Yaying Hu, Lei Deng, Haifeng Zheng, Xinxin Feng, and Youjia Chen. 2020. Network latency estimation with Graph- Laplacian regularization tensor completion. In GLOBECOM 2020-2020 IEEE Global Communications Conference . IEEE, J. ACM, Vol. 37, No. 4, Article 111. Publication date:...

2020
[18]

Ajil Jalal, Liu Liu, Alexandros G Dimakis, and Constantine Caramanis. 2020. Robust compressed sensing using gen- erative models. Advances in Neural Information Processing Systems 33 (2020), 713–727

2020
[19]

Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2018. Progressive Growing of GANs for Improved Quality, Stability, and Variation. In International Conference on Learning Representations

2018
[20]

Kilmer and Carla D

Misha E. Kilmer and Carla D. Martin. 2011. Factorization strate gies for third-order tensors. Linear Algebra and Its Applications 435, 3 (2011), 641–658. doi:10.1016/j.laa.2010.09.020

2011 doi
[21]

Huiyu Lin, Lei Deng, Lingzhen Wang, Haifeng Zheng, and Xinxin Feng. 2022. Robust Spatial-Temporal Graph-Tensor Recovery for Network Latency Estimation. In GLOBECOM 2022-2022 IEEE Global Communications Conference . IEEE, 4202–4207

2022
[22]

Xiao-Yang Liu and Xiaodong Wang. 2021. Real-Time Indoor Localizat ion for Smartphones Using Tensor- Generative Adversarial Nets. IEEE Transactions on Neural Networks and Learning Systems 32, 8 (2021), 3433–3443. doi:10.1109/TNNLS.2020.3010724

2021
[23]

Xiao-Yang Liu, Xiaodong Wang, Bo Yuan, and Jiashu Han. 2025. Spectr al Tensor Layers for Communication-Free Distributed Deep Learning. IEEE Transactions on Neural Networks and Learning Systems 36, 4 (2025), 7237–7251. doi:10.1109/TNNLS.2024.3394861

2025
[24]

Yipeng Liu. 2021. Tensors for data processing: theory, methods, and applicat ions. Academic Press

2021
[25]

Canyi Lu, Jiashi Feng, Yudong Chen, Wei Liu, Zhouchen Lin, and Shuic heng Yan. 2019. Tensor robust principal component analysis with a new tensor nuclear norm. IEEE transactions on pattern analysis and machine intellig ence 42, 4 (2019), 925–938

2019
[26]

Canyi Lu, Xi Peng, and Yunchao Wei. 2019. Low-rank tensor comple tion with a new tensor nuclear norm induced by invertible linear transforms. In Proceedings of the IEEE/CVF conference on computer vision a nd pattern recognition . 5996–6004

2019
[27]

Hao Mei, Junxian Li, Zhiming Liang, Guanjie Zheng, Bin Shi, and Hua Wei. 202 3. Uncertainty-aware traﬃc prediction under missing data. In 2023 IEEE International Conference on Data Mining (ICDM) . IEEE, 1223–1228

2023
[28]

S Mekaouil, C Benhamed, and K Ghoumid. 2013. Traﬃc matrix estim ation using the Levenberg-Marquardt neural network of a large IP system. Data Management and Security: Applications in Medicine, Sc iences and Engineering 45 (2013), 85–95

2013
[29]

H Zare Moayedi and MA Masnadi-Shirazi. 2008. ARIMA model for ne twork traﬃc prediction and anomaly detection. In 2008 international symposium on information technology , Vol. 4. IEEE, 1–6

2008
[30]

Elizabeth Newman, Lior Horesh, Haim Avron, and Misha Kilmer. 201 8. Stable tensor neural networks for rapid deep learning. arXiv preprint arXiv:1811.06569 (2018)

2018 arXiv
[31]

Shyam Nihale, Shantanu Sharma, Lokesh Parashar, and Upendra Singh. 2020. Network Traﬃc Prediction Using Long Short-Term Memory. In 2020 International Conference on Electronics and Sustaina ble Communication Systems (ICESC) . 338–343. doi:10.1109/ICESC48915.2020.9156045

2020
[32]

OpenAI. 2023. Gpt-4 technical report. https://cdn.openai.c om/papers/gpt-4.pdf

2023
[33]

Chandrasen Pandey, Vaibhav Tiwari, Joel JPC Rodrigues, and Dipt endu Sinha Roy. 2024. 5GT-GAN-NET: Internet Traﬃc Data Forecasting With Supervised Loss Based Synthetic Data Over 5G. IEEE Transactions on Mobile Computing (2024)

2024
[34]

Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsuper vised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)

2015 arXiv
[35]

Sajal Saha, Anwar Haque, and Greg Sidebottom. 2023. Analyzing the impact of outlier data points on multi-step Internet traﬃc prediction using deep sequence models. IEEE Transactions on Network and Service Management 20, 2 (2023), 1345–1362

2023
[36]

Lizhuang Tan, Wei Su, Wei Zhang, Jianhui Lv, Zhenyi Zhang, Jingying Mia o, Xiaoxi Liu, and Na Li. 2021. In-band network telemetry: A survey. Computer Networks 186 (2021), 107763

2021
[37]

Lizhuang Tan, Wei Su, Wei Zhang, Huiling Shi, Jingying Miao, and Pilar Manz anares-Lopez. 2021. A packet loss monitoring system for in-band network telemetry: detection, localizat ion, diagnosis and recovery. IEEE Transactions on Network and Service Management 18, 4 (2021), 4151–4168

2021
[38]

Steve Uhlig, Bruno Quoitin, Jean Lepropre, and Simon Balon. 2006 . Providing public intradomain traﬃc matrices to the research community. ACM SIGCOMM Computer Communication Review 36, 1 (2006), 83–86

2006
[39]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)

2017
[40]

Ravi Vinayakumar, KP Soman, and Prabaharan Poornachandran. 20 17. Applying deep learning approaches for net- work traﬃc prediction. In 2017 International Conference on Advances in Computing, Co mmunications and Informatics (ICACCI). IEEE, 2353–2358. J. ACM, Vol. 37, No. 4, Art...

2017
[41]

Lingzhen Wang, Lei Deng, Xinxin Feng, and Haifeng Zheng. 2021. Robust Low-Tubal-Rank Tensor Completion for Network Latency Estimation and Anomaly Detection. In 2021 13th International Conference on Wireless Communica- tions and Signal Processing (WCSP) . IEEE, 1–5

2021
[42]

Yan Wu, Mihaela Rosca, and Timothy Lillicrap. 2019. Deep compressed sensing. In International Conference on Machine Learning. PMLR, 6850–6860

2019
[43]

Kun Xie, Yudian Ouyang, Xin Wang, Gaogang Xie, Kenli Li, Wei Liang, Jia nnong Cao, and Jigang Wen. 2023. Deep Adversarial Tensor Completion for Accurate Network Traﬃc Meas urement. IEEE/ACM Transactions on Networking (2023)

2023
[44]

Kun Xie, Lele Wang, Xin Wang, Gaogang Xie, Jigang Wen, Guangxing Zhang, Jiannong Cao, and Dafang Zhang
[45]

Ruotian Xie, Jigang Wen, Xiaodi Chen, Kun Xie, Wei Liang, Naixue Xiong, Dafang Zhang, Gaogang Xie, and Kenli Li. 2024. M 2STL: Multi-Range Multi-Level Spatial-Temporal Learning Model for Network Traﬃc Prediction. IEEE Transactions on Network Science and Engineering (2024)

2024
[46]

Shuona Xu and Biqing Zeng. 2014. Network traﬃc prediction model b ased on auto-regressive moving average.Journal of Networks 9, 3 (2014), 653

2014
[47]

Shuo Yang, Minjing Dong, Yunhe Wang, and Chang Xu. 2023. Adversarial Recurrent Time Series Imputation. IEEE Transactions on Neural Networks and Learning Systems 34, 4 (2023), 1639–1650. doi:10.1109/TNNLS.2020.3010524

2023
[48]

Zemin Zhang and Shuchin Aeron. 2016. Exact tensor completion us ing t-SVD. IEEE Transactions on Signal Processing 65, 6 (2016), 1511–1526

2016
[49]

Rui Zhu, Bang Liu, Di Niu, Zongpeng Li, and Hong Vicky Zhao. 2016. Ne twork latency estimation for personal devices: A matrix completion approach. IEEE/ACM Transactions on Networking 25, 2 (2016), 724–737. A Empirical Study The tensor low-rank properties of network traﬃc data ...

2016
[2018]

IEEE/ACM Transactions on Networking 26, 2 (2018), 793–806

Accurate recovery of Internet traﬃc data: A sequential tens or completion approach. IEEE/ACM Transactions on Networking 26, 2 (2018), 793–806

2018
[2019]

In IEEE INFOCOM 2019- IEEE Conference on Computer Communications Workshops (INF OCOM WKSHPS)

Network traﬃc prediction based on diﬀusion convolutional rec urrent neural networks. In IEEE INFOCOM 2019- IEEE Conference on Computer Communications Workshops (INF OCOM WKSHPS). IEEE, 246–251

2019

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [6]

Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis. 201 7. Compressed sensing using generative models. In International conference on machine learning . PMLR, 537–546

work page

[2] [10]

Lei Deng, Xiao-Yang Liu, Haifeng Zheng, Xinxin Feng, and Zhizhang Chen. 2 023. Graph-Tensor Neural Networks for Network Traﬃc Data Imputation. IEEE/ACM Transactions on Networking(2023), 1–15. doi:10.1109/TNET.2023.3268982

work page arXiv 2023

[3] [1]

Imad Alawe, Adlen Ksentini, Yassine Hadjadj-Aoul, and Philippe Be rtin. 2018. Improving traﬃc forecasting for 5G core network scalability: A machine learning approach. IEEE Network 32, 6 (2018), 42–49

work page 2018

[4] [2]

Davide Andreoletti, Sebastian Troia, Francesco Musumeci, Silvia Giordano, Guido Maier, and Massimo Tornatore

work page

[5] [3]

Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Mat thew W Hoﬀman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas. 2016. Learning to learn by g radient descent by gradient descent. In Proceedings of the 30th International Conference on Neural Information Processing Systems. 3988–3996

work page 2016

[6] [4]

Ons Aouedi, Van An Le, Kandaraj Piamrat, and Yusheng Ji. 2025. Dee p learning on network traﬃc prediction: Recent advances, analysis, and future directions. Comput. Surveys 57, 6 (2025), 1–37

work page 2025

[7] [5]

Piotr Bojanowski, Armand Joulin, David Lopez-Pas, and Arthur Szl am. 2018. Optimizing the Latent Space of Genera- tive Networks. In International Conference on Machine Learning . PMLR, 600–609

work page 2018

[8] [7]

Maolin Che, Xuezhong Wang, Yimin Wei, and Xile Zhao. 2022. Fast rand omized tensor singular value thresholding for low-rank tensor optimization. Numerical Linear Algebra with Applications 29, 6 (2022), e2444

work page 2022