Pith. sign in

REVIEW 3 major objections 5 minor 51 references

STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read STTS-EAD embeds anomaly detection into the training loop of a spatio-temporal forecasting model and reports consistent RMSE reductions of 5.6%, 3.8%, and 8.1% over the best baselines on three datasets.

desk verdict Plausible idea, but thin evidence and a self-referential cleaning loop make the strong claims hard to trust—worth a serious referee, not a pass. read the letter →

arxiv 2501.07814 v1 pith:X6VCWQI7 submitted 2025-01-14 cs.LG cs.AI

Yuanyuan Liang , Tianhao Zhang , Tingyu Xie This is my paper · ORCID
classification cs.LGcs.AI
keywords multivariatetimeseriesforecastinganomalydetectionspatio-temporallearningembeddeddatacleaningattentionmechanismsalesstockpriceprediction
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 anomaly handling for multivariate time series forecasters should not be a separate preprocessing stage: the forecasting model itself can score its own training points during training, flag likely anomalies, and replace them with smoothed values, and alternating this cleaning with model updates improves forecast accuracy. To make the case, it introduces STTS-EAD, a spatio-temporal forecasting model with an embedded anomaly-detection module, and reports RMSE improvements of 5.6%, 3.8%, and 8.1% over the strongest baseline on coffee-sales and S&P 500 datasets. If correct, the result turns data cleaning from an offline statistical chore into a trainable component that can use the same spatio-temporal context as the predictor.

What carries the argument

The load-bearing mechanism is a residual-score feedback loop the paper calls the Embedded Anomaly Detection (EAD) module. Every $\eta$ training epochs the module computes, for each training point, an anomaly score $s_i = \delta \epsilon^p_i + (1-\delta) \epsilon^r_i$ from the current model's prediction error and reconstruction error, sets a threshold with nonparametric dynamic thresholding, and replaces points above the threshold with smoothed filler data. The cleaned set is then used to continue training the STTS forecaster, which itself supplies the residuals, so detection and forecasting are repeatedly optimized against each other. Supporting machinery includes concatenated temporal and spatial embeddings (with a GCN when a prior graph exists), a correlation-based auxiliary series selection that keeps the input dimension fixed, and temporal/spatial attention plus a feature-wise transformer and LSTM that produce the prediction and reconstruction outputs.

What would settle it

Run STTS-EAD on synthetic multivariate time series with known injected anomalies of varying size and record whether the flagged positions match the injections and whether cleaning recovers the true signal; if flagged sets are mostly noise or if equally many random points smoothed produce the same RMSE gains, the reported improvements do not come from anomaly detection.

Watch

Extended reading notes

Core claim

The paper's central claim is that integrating anomaly detection into the training phase, rather than running it as a separate preprocessing step, improves multivariate time series forecasting accuracy. STTS-EAD alternates between optimizing a spatio-temporal prediction model and running an Embedded Anomaly Detection module every few epochs: the module takes prediction and reconstruction residuals from the current model, combines them into a weighted anomaly score, thresholds the scores with a nonparametric dynamic method, and replaces flagged training points with smooth filler data. The cleaned dataset then feeds the next round of training. The paper reports that this procedure lowers RMSE by 5.6% on Coffee-Bean, 3.8% on Coffee-Cream, and 8.1% on Stock-SP500 relative to the best baseline, and that the same embedding of anomaly detection beats three statistical and deep-learning preprocessing alternatives when each is attached to the same STTS base model.

Load-bearing premise

The training loop assumes the model's own prediction and reconstruction errors reliably mark the truly anomalous points, and that replacing those points with smoothed values improves the data, even though no ground-truth anomaly labels are used to verify either step.

Editorial extensions

If this is right

  • If the reported gains hold, two-stage pipelines that clean data before training should be revisited: cleaning inside the training loop can outperform 3-sigma, EWMA, USAD, and LSTM-NDT preprocessing on the same base forecaster.
  • Combining prediction and reconstruction residuals in the anomaly score performs better than either residual alone across the three datasets, so models that produce both outputs are natural hosts for this style of embedded cleaning.
  • The auxiliary series selection module lets the model handle a changing number of time series without retraining, because the input dimension is fixed by the selected M series.
  • Every ablated STTS component—auxiliary selection, spatial attention, temporal attention, feature-wise transformer, and LSTM—increases error, supporting the claim that spatio-temporal structure drives the gains.

Reading between the lines

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

  • A direct extension, not tested in the paper: the same EAD loop could be bolted onto other forecasters that already produce prediction and reconstruction errors, without the STTS architecture, to test whether the cleaning mechanism transfers.
  • Because the model labels its own training data, early epochs may flag ordinary noise or hard-but-informative points; a warm-up schedule that activates EAD later, or that trusts scores only after validation stabilizes, is a natural robustness check.
  • The stock dataset has no prior graph, yet STTS-EAD still improves, suggesting learned embeddings can substitute for explicit spatial structure; injecting controlled anomalies into synthetic data would let one measure detection precision directly.
  • The reported gains may partly reflect regularization from smoothing rather than accurate anomaly identification; comparing against random smoothing of the same number of points would separate those effects.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes STTS-EAD, a multivariate time series forecasting method that embeds an anomaly detection module into the training loop. The STTS model learns temporal and spatial embeddings, selects auxiliary series, and combines attention, transformer, and LSTM components for prediction and reconstruction. The EAD module periodically computes prediction and reconstruction residuals, labels high-residual points as anomalies using a nonparametric dynamic threshold, replaces them with smoothed values, and retrains the model on the cleaned data. Experiments on two proprietary coffee sales datasets and the public Stock-SP500 dataset report RMSE/MAE improvements over nine baselines, with claimed RMSE gains of 5.6%, 3.8%, and 8.1% over the best baselines on the three datasets. The paper also includes ablations of the STTS components and a comparison of EAD with classical and deep anomaly-preprocessing methods.

Significance. If the mechanism holds, the central idea of coupling anomaly-driven data correction with forecasting training in a spatio-temporal model is of practical interest, and the dynamic adaptation to a varying number of time series is a useful property. The paper's strength is the comprehensive set of baselines and ablations, and the explicit comparison of EAD with two-stage preprocessing alternatives. However, the verification is limited: the two primary datasets are proprietary, the reported gains are not accompanied by error bars or significance tests, and the anomaly detection component is not validated against ground truth. The result is therefore promising but not yet convincing as stated.

major comments (3)
  1. [Table II (Parts 1 and 3), Section IV.B and IV.D.1] The numerical results for the same model are inconsistent: STTS-EAD Coffee-Cream RMSE is reported as 1.09157 and MAE 0.74425 in Part 1, but as 1.09557 and 0.74919 in Part 3. With the STTS baseline at 1.11719, the claimed 3.8% gain in Part 1 becomes approximately 1.9% if the Part 3 value is used. This discrepancy is comparable in magnitude to the reported improvement and must be reconciled, and the paper should report means and standard deviations over multiple random seeds to establish that the gains are not run-to-run artifacts.
  2. [Section III.C, Algorithm 1, and Section IV.D] The EAD module labels training points as anomalous using the current model's own prediction and reconstruction residuals, then replaces them and retrains. The paper never validates these labels: no ground-truth anomaly annotations, no synthetic anomaly injection, and no precision/recall or F1 analysis. Consequently, the reported gains could stem from generic smoothing of high-residual (possibly hard-to-fit or noisy) points rather than from genuinely detecting anomalies. The ablation in Part 3 of Table II compares against other smoothing-based preprocessing methods, but it does not include a control that smooths the same number of randomly selected points. Such a control (or a synthetic injection study) is necessary to support the claim that EAD is performing anomaly detection rather than a generic regularizer.
  3. [Section IV.B, Table II] The paper states that STTS-EAD 'significantly outperform[s] baselines,' but no statistical significance tests or error bars are provided. The improvement over STTS on Stock-SP500 is only 0.65% in RMSE (0.01231 vs 0.01223), and the Coffee-Cream inconsistency noted above shows that variability can be of the same order as the reported gains. The authors should report means and standard deviations across multiple runs and, where appropriate, a paired significance test against the strongest baseline.
minor comments (5)
  1. [Table II, Part 3] The arrows (↑ and ↓) in Part 3 are not defined in the caption, and their direction is unintuitive (a lower RMSE is marked ↑). Please clarify the notation or use a different marker.
  2. [Algorithm 1, line 5] The notation {ε^p_1, ..., ε^p_{N×T}} is ambiguous: are these errors defined per time series and per timestamp, and if so how are they aligned with the sliding-window inputs used by the model? Please define the indexing explicitly.
  3. [Section III.C] The 'nonparametric dynamic thresholding method [30]' is invoked without describing how it is applied to the anomaly scores. A brief description of the threshold computation (e.g., the smoothing and extreme-value steps from Hundman et al.) would improve reproducibility.
  4. [Abstract and Section I] The claim that STTS-EAD is 'the first to integrate anomaly detection and forecasting tasks in the training phase' is a strong novelty claim that is not supported by a dedicated survey of training-time cleaning or robust learning approaches. Please narrow the claim to the specific residual-based self-cleaning framework proposed here, or add a more thorough comparison with prior joint training schemes.
  5. [Section IV.D.3, Figure 3] The caption 'Performance with different filling Data. (RMSE)' should state that lower values are better and specify that the methods compared are 'mean', 'period mean', 'lowess', and 'remove'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the EAD feedback loop is a validation confound, not a circular derivation.

full rationale

The central claim is that STTS-EAD improves multivariate time series forecasting accuracy, evaluated on held-out test sets (Table I, II) against nine external baselines. That comparison is not circular: the forecasting errors are measured on data not used for training, and the improvement is an empirical result rather than a consequence of the definitions. The EAD module does create a self-referential loop (Algorithm 1: residual scores from the current STTS model are thresholded, the flagged points are replaced with smoothed values, and the same model is retrained on the cleaned data). However, this is a data-cleaning heuristic, not a derivation in which the predicted quantity is defined as the fitted input. The paper does not claim to validate anomaly detections against ground-truth labels, and the absence of such validation is a limitation: the observed gains could come from generic smoothing rather than from removing genuine anomalies. That is an identification/correctness concern, not a circularity. There are no load-bearing self-citations: the thresholding method [30] and other baselines are external. A separate numerical inconsistency exists (Coffee-Cream RMSE for STTS-EAD is 1.09157 in Table II Part 1 but 1.09557 in Part 3), which should be corrected, but it is a reporting issue, not evidence of circularity. Overall, no step in the paper's argument reduces, by construction or by self-citation, to its own input.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities, forces, or conserved quantities. Its central claim rests on six manually chosen hyperparameters and three domain assumptions about residual-based anomaly detection, smoothing-based cleaning, and dataset representativeness.

free parameters (6)
  • gamma
    Momentum weight for temporal embedding in Eq. 3, chosen by hand, not reported.
  • delta = 0.5 (Coffee-Bean, Coffee-Cream); 0.8 (Stock-SP500)
    Weights prediction vs reconstruction error in anomaly score; tuned per dataset as shown in Table III.
  • beta
    Loss weight between prediction and reconstruction in Eq. 7, not specified.
  • eta
    EAD execution period in Algorithm 1, not specified.
  • M
    Number of selected auxiliary series in Section III-B2, manually selected hyperparameter.
  • d1, d2
    Dimensions of temporal and spatial embeddings in Section III-B1, not reported.
assumptions (3)
  • domain assumption Residuals from the current model (prediction and reconstruction errors) are reliable indicators of true data anomalies.
    The EAD module uses score = delta * ep + (1-delta) * er to label points as anomalous. If model errors are not anomaly-correlated, the cleaning step removes informative samples. This is stated in Section III-C and Algorithm 1, lines 4-10.
  • domain assumption Replacing detected anomalies with smooth data (mean, period mean, lowess) improves training data quality.
    Section IV-D3 compares four filling methods on three datasets and concludes smoothing helps, but no general justification is given; the choice of filling method also varies by dataset.
  • domain assumption The three datasets (two proprietary coffee sales, one public stock) are representative multivariate time series forecasting benchmarks.
    The empirical claims rest entirely on these datasets; no public benchmark such as ETT, M4, or Traffic is used.

how reviews work

0 comments
Cite this review

Pith. "Pith review of STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via." pith.science (2026). https://pith.science/paper/X6VCWQI7

@misc{pith2026250107814,
  author       = {Pith},
  title        = {Pith review of: STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X6VCWQI7}},
  note         = {Machine review of arXiv:2501.07814}
}
read the original abstract

Handling anomalies is a critical preprocessing step in multivariate time series prediction. However, existing approaches that separate anomaly preprocessing from model training for multivariate time series prediction encounter significant limitations. Specifically, these methods fail to utilize auxiliary information crucial for identifying latent anomalies associated with spatiotemporal factors during the preprocessing stage. Instead, they rely solely on data distribution for anomaly detection, which can result in the incorrect processing of numerous samples that could otherwise contribute positively to model training. To address this, we propose STTS-EAD, an end-to-end method that seamlessly integrates anomaly detection into the training process of multivariate time series forecasting and aims to improve Spatio-Temporal learning based Time Series prediction via Embedded Anomaly Detection. Our proposed STTS-EAD leverages spatio-temporal information for forecasting and anomaly detection, with the two parts alternately executed and optimized for each other. To the best of our knowledge, STTS-EAD is the first to integrate anomaly detection and forecasting tasks in the training phase for improving the accuracy of multivariate time series forecasting. Extensive experiments on a public stock dataset and two real-world sales datasets from a renowned coffee chain enterprise show that our proposed method can effectively process detected anomalies in the training stage to improve forecasting performance in the inference stage and significantly outperform baselines.

Figures

Figures reproduced from arXiv: 2501.07814 by the authors.

Figure 1
Figure 1. Taxonomy of anomaly detection methods for different purposes and [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The architecture of STTS-EAD, with a spatio-temporal based prediction model and an anomaly detection module. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Case Study on anomaly detection and filling in the EAD module. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 24 canonical work pages

  1. [1]

    Domain fusion cnn-lstm for short-term power consumption forecasting,

    X. Shao, C. Pu, Y . Zhang, and C. S. Kim, “Domain fusion cnn-lstm for short-term power consumption forecasting,” IEEE Access , vol. 8, pp. 188 352–188 362, 2020

    work page 2020

  2. [2]

    Stock price forecast based on cnn-bilstm-eca model,

    Y . Chen, R. Fang, T. Liang, Z. Sha, S. Li, Y . Yi, W. Zhou, and H. Song, “Stock price forecast based on cnn-bilstm-eca model,” Scientific Pro- gramming, vol. 2021, pp. 1–20, 2021

    work page 2021

  3. [3]

    Supply chain sales forecasting based on lightgbm and lstm combination model,

    T. Weng, W. Liu, and J. Xiao, “Supply chain sales forecasting based on lightgbm and lstm combination model,” Industrial Management & Data Systems, vol. 120, no. 2, pp. 265–279, 2020

    work page 2020

  4. [4]

    Multivariate time series anomaly detection and interpretation using hierarchical inter- metric and temporal embedding,

    Z. Li, Y . Zhao, J. Han, Y . Su, R. Jiao, X. Wen, and D. Pei, “Multivariate time series anomaly detection and interpretation using hierarchical inter- metric and temporal embedding,” in Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining , 2021, pp. 3220–3230

    work page 2021

  5. [5]

    St-norm: Spatial and temporal normalization for multi-variate time series forecasting,

    J. Deng, X. Chen, R. Jiang, X. Song, and I. W. Tsang, “St-norm: Spatial and temporal normalization for multi-variate time series forecasting,” in Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining , 2021, pp. 269–278

    work page 2021

  6. [6]

    Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles,

    K. Li, C. Ye, Z. Yang, A. Carass, S. H. Ying, and J. L. Prince, “Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles,” in Medical Imaging 2016: Image Processing, vol. 9784. SPIE, 2016, pp. 398–404

    work page 2016

  7. [7]

    Outlier detection in weight time series of connected scales,

    S. Mehrang, E. Helander, M. Pavel, A. Chieh, and I. Korhonen, “Outlier detection in weight time series of connected scales,” in 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, 2015, pp. 1489–1496

    work page 2015

  8. [8]

    G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control . John Wiley & Sons, 2015

    2015

Show all 51 references
  1. [9]

    Bidirectional recurrent neural net- works,

    M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural net- works,” IEEE transactions on Signal Processing , vol. 45, no. 11, pp. 2673–2681, 1997

    1997

  2. [10]

    An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,

    S. Bai, J. Z. Kolter, and V . Koltun, “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv preprint arXiv:1803.01271, 2018

    2018 arXiv

  3. [11]

    Wavenet: A gener- ative model for raw audio,

    A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “Wavenet: A gener- ative model for raw audio,” arXiv preprint arXiv:1609.03499 , 2016

    2016 arXiv

  4. [12]

    Modeling long-and short-term temporal patterns with deep neural networks,

    G. Lai, W.-C. Chang, Y . Yang, and H. Liu, “Modeling long-and short-term temporal patterns with deep neural networks,” in The 41st international ACM SIGIR conference on research & development in information retrieval, 2018, pp. 95–104

    2018

  5. [13]

    Convolutional lstm network: A machine learning approach for precipitation nowcasting,

    X. Shi, Z. Chen, H. Wang, D.-Y . Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” Advances in neural information processing systems, vol. 28, 2015

    2015

  6. [14]

    Timesnet: Temporal 2d-variation modeling for general time series analysis,

    H. Wu, T. Hu, Y . Liu, H. Zhou, J. Wang, and M. Long, “Timesnet: Temporal 2d-variation modeling for general time series analysis,” arXiv preprint arXiv:2210.02186, 2022

    2022 arXiv

  7. [15]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

    2017

  8. [16]

    Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting,

    H. Wu, J. Xu, J. Wang, and M. Long, “Autoformer: Decomposition trans- formers with auto-correlation for long-term series forecasting,”Advances in Neural Information Processing Systems , vol. 34, pp. 22 419–22 430, 2021

    2021

  9. [17]

    Preformer: predictive transformer with multi- scale segment-wise correlations for long-term time series forecasting,

    D. Du, B. Su, and Z. Wei, “Preformer: predictive transformer with multi- scale segment-wise correlations for long-term time series forecasting,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

    2023

  10. [18]

    Fedformer: Frequency enhanced decomposed transformer for long-term series fore- casting,

    T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, and R. Jin, “Fedformer: Frequency enhanced decomposed transformer for long-term series fore- casting,” in International Conference on Machine Learning . PMLR, 2022, pp. 27 268–27 286

    2022

  11. [19]

    Informer: Beyond efficient transformer for long sequence time-series forecasting,

    H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in Proceedings of the AAAI conference on artificial intel- ligence, vol. 35, no. 12, 2021, pp. 11 106–11 115

    2021

  12. [20]

    Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting,

    S. Liu, H. Yu, C. Liao, J. Li, W. Lin, A. X. Liu, and S. Dustdar, “Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting,” inInternational conference on learning representations, 2021

    2021

  13. [21]

    Are transformers effective for time series forecasting?

    A. Zeng, M. Chen, L. Zhang, and Q. Xu, “Are transformers effective for time series forecasting?” in Proceedings of the AAAI conference on artificial intelligence, vol. 37, no. 9, 2023, pp. 11 121–11 128

    2023

  14. [22]

    Less is more: Fast multivariate time series forecasting with light sampling- oriented mlp structures,

    T. Zhang, Y . Zhang, W. Cao, J. Bian, X. Yi, S. Zheng, and J. Li, “Less is more: Fast multivariate time series forecasting with light sampling- oriented mlp structures,” arXiv preprint arXiv:2207.01186 , 2022

    2022 arXiv

  15. [23]

    Mts-mixers: Multivariate time series forecasting via factorized temporal and channel mixing,

    Z. Li, Z. Rao, L. Pan, and Z. Xu, “Mts-mixers: Multivariate time series forecasting via factorized temporal and channel mixing,” arXiv preprint arXiv:2302.04501, 2023

    2023 arXiv

  16. [24]

    Long-term forecasting with tide: Time-series dense encoder,

    A. Das, W. Kong, A. Leach, R. Sen, and R. Yu, “Long-term forecasting with tide: Time-series dense encoder,” arXiv preprint arXiv:2304.08424, 2023

    2023 arXiv

  17. [25]

    Con- necting the dots: Multivariate time series forecasting with graph neural networks,

    Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang, “Con- necting the dots: Multivariate time series forecasting with graph neural networks,” in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining , 2020, pp. 753–763

    2020

  18. [26]

    Spectral temporal graph neural network for multivariate time-series forecasting,

    D. Cao, Y . Wang, J. Duan, C. Zhang, X. Zhu, C. Huang, Y . Tong, B. Xu, J. Bai, J. Tong et al. , “Spectral temporal graph neural network for multivariate time-series forecasting,” Advances in neural information processing systems, vol. 33, pp. 17 766–17 778, 2020

    2020

  19. [27]

    Graph wavenet for deep spatial-temporal graph modeling,

    Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” arXiv preprint arXiv:1906.00121 , 2019

    1906 arXiv

  20. [28]

    Spatial temporal graph convolutional networks for skeleton-based action recognition,

    S. Yan, Y . Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton-based action recognition,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018

    2018

  21. [29]

    A review on outlier/anomaly detection in time series data,

    A. Bl ´azquez-Garc´ıa, A. Conde, U. Mori, and J. A. Lozano, “A review on outlier/anomaly detection in time series data,” ACM Computing Surveys (CSUR), vol. 54, no. 3, pp. 1–33, 2021

    2021

  22. [30]

    Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,

    K. Hundman, V . Constantinou, C. Laporte, I. Colwell, and T. Soder- strom, “Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding,” in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining , 2018, pp. 387–395

    2018

  23. [31]

    Deepant: A deep learning approach for unsupervised anomaly detection in time series,

    M. Munir, S. A. Siddiqui, A. Dengel, and S. Ahmed, “Deepant: A deep learning approach for unsupervised anomaly detection in time series,” Ieee Access, vol. 7, pp. 1991–2005, 2018

    1991

  24. [32]

    Online learning of contextual hidden markov models for temporal-spatial data analysis,

    Y . Zhou, R. Arghandeh, and C. J. Spanos, “Online learning of contextual hidden markov models for temporal-spatial data analysis,” in 2016 IEEE 55th Conference on Decision and Control (CDC) . IEEE, 2016, pp. 6335–6341

    2016

  25. [33]

    Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data,

    D. T. Shipmon, J. M. Gurevitch, P. M. Piselli, and S. T. Edwards, “Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data,” arXiv preprint arXiv:1708.03665 , 2017

    2017 arXiv

  26. [34]

    Lstm-based encoder-decoder for multi-sensor anomaly de- tection,

    P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, “Lstm-based encoder-decoder for multi-sensor anomaly de- tection,” arXiv preprint arXiv:1607.00148 , 2016

    2016 arXiv

  27. [35]

    Robust anomaly detection for multivariate time series through stochastic recurrent neural network,

    Y . Su, Y . Zhao, C. Niu, R. Liu, W. Sun, and D. Pei, “Robust anomaly detection for multivariate time series through stochastic recurrent neural network,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , 2019, pp. 2828– 2837

    2019

  28. [36]

    A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data,

    C. Zhang, D. Song, Y . Chen, X. Feng, C. Lumezanu, W. Cheng, J. Ni, B. Zong, H. Chen, and N. V . Chawla, “A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data,” in Proceedings of the AAAI conference on artificial intelligence ...

    2019

  29. [37]

    Multivariate time-series anomaly detection via graph attention network,

    H. Zhao, Y . Wang, J. Duan, C. Huang, D. Cao, Y . Tong, B. Xu, J. Bai, J. Tong, and Q. Zhang, “Multivariate time-series anomaly detection via graph attention network,” in 2020 IEEE International Conference on Data Mining (ICDM) . IEEE, 2020, pp. 841–850

    2020

  30. [38]

    Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,

    T. Schlegl, P. Seeb ¨ock, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs, “Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,” in International conference on information processing in medical imaging . Springer, 2017, pp. 146– 157

    2017

  31. [39]

    Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks,

    D. Li, D. Chen, B. Jin, L. Shi, J. Goh, and S.-K. Ng, “Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks,” in International conference on artificial neural networks. Springer, 2019, pp. 703–716

    2019

  32. [40]

    Tanogan: Time series anomaly detection with generative adversarial networks,

    M. A. Bashar and R. Nayak, “Tanogan: Time series anomaly detection with generative adversarial networks,” in 2020 IEEE Symposium Series on Computational Intelligence (SSCI) . IEEE, 2020, pp. 1778–1785

    2020

  33. [41]

    Usad: Unsupervised anomaly detection on multivariate time series,

    J. Audibert, P. Michiardi, F. Guyard, S. Marti, and M. A. Zuluaga, “Usad: Unsupervised anomaly detection on multivariate time series,” in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining , 2020, pp. 3395–3404

    2020

  34. [42]

    Daemon: Unsupervised anomaly detection and interpretation for multivariate time series,

    X. Chen, L. Deng, F. Huang, C. Zhang, Z. Zhang, Y . Zhao, and K. Zheng, “Daemon: Unsupervised anomaly detection and interpretation for multivariate time series,” in 2021 IEEE 37th International Confer- ence on Data Engineering (ICDE) . IEEE, 2021, pp. 2225–2230

    2021

  35. [43]

    Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,

    W. Li, W. Mo, X. Zhang, J. J. Squiers, Y . Lu, E. W. Sellke, W. Fan, J. M. DiMaio, and J. E. Thatcher, “Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging,” Journal of biomedical optics , vol. 20, no. 12, pp. 1...

    2015

  36. [44]

    Automatic outlier detection for time series: an application to sensor data,

    S. Basu and M. Meckesheimer, “Automatic outlier detection for time series: an application to sensor data,” Knowledge and Information Systems, vol. 11, pp. 137–154, 2007

    2007

  37. [45]

    Automated load curve data cleansing in power systems,

    J. Chen, W. Li, A. Lau, J. Cao, and K. Wang, “Automated load curve data cleansing in power systems,” IEEE Transactions on Smart Grid , vol. 1, no. 2, pp. 213–221, 2010

    2010

  38. [46]

    Probabilistic reasoning for streaming anomaly detection,

    K. M. Carter and W. W. Streilein, “Probabilistic reasoning for streaming anomaly detection,” in 2012 IEEE Statistical Signal Processing Work- shop (SSP). IEEE, 2012, pp. 377–380

    2012

  39. [47]

    Screen: stream data cleaning under speed constraints,

    S. Song, A. Zhang, J. Wang, and P. S. Yu, “Screen: stream data cleaning under speed constraints,” in Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data , 2015, pp. 827–841

    2015

  40. [48]

    Sequential data cleaning: A statistical approach,

    A. Zhang, S. Song, and J. Wang, “Sequential data cleaning: A statistical approach,” in Proceedings of the 2016 International Conference on Management of Data , 2016, pp. 909–924

    2016

  41. [49]

    Semi-supervised classification with graph convolutional networks,

    T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907 , 2016

    2016 arXiv

  42. [50]

    How attentive are graph attention networks?

    S. Brody, U. Alon, and E. Yahav, “How attentive are graph attention networks?” arXiv preprint arXiv:2105.14491 , 2021

    2021 arXiv

  43. [51]

    Long short-term memory,

    S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997

    1997

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.