mlsanityData to 7 Oct 2026

RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies

arXiv 2402.02032 ↗First posted 3 Feb 2024cs.LGcited by 17

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Abstract (arXiv)

Time series forecasting is an important and forefront task in many real-world applications. However, most of time series forecasting techniques assume that the training data is clean without anomalies. This assumption is unrealistic since the collected time series data can be contaminated in practice. The forecasting model will be inferior if it is directly trained by time series with anomalies. Thus it is essential to develop methods to automatically learn a robust forecasting model from the contaminated data. In this paper, we first statistically define three types of anomalies, then theoretically and experimentally analyze the loss robustness and sample robustness when these anomalies exist. Based on our analyses, we propose a simple and efficient algorithm to learn a robust forecasting model. Extensive experiments show that our method is highly robust and outperforms all existing approaches. The code is available at https://github.com/haochenglouis/RobustTSF.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 33 of its 48 references that are in the map. First posted on arXiv 3 Feb 2024.

Builds on

33 of 48 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  4. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  5. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  6. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  7. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,053
  8. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  9. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 994
  10. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  11. 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
  12. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 663
21 more
  1. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 652
  2. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 580
  3. 2020Learning under Concept Drift: A Reviewcited by 470
  4. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
  5. 2017Robust Loss Functions under Label Noise for Deep Neural Networkscited by 441
  6. 2020Normalized Loss Functions for Deep Learning with Noisy Labelscited by 257
  7. 2020A review on outlier/anomaly detection in time series datacited by 187
  8. 2016Structured Inference Networks for Nonlinear State Space Modelscited by 182
  9. 2020Learning with Instance-Dependent Label Noise: A Sample Sieve Approachcited by 158
  10. 2019High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processescited by 147
  11. 2019Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Ratescited by 142
  12. 2020A Second-Order Approach to Learning with Instance-Dependent Label Noisecited by 90
  13. 2021Clusterability as an Alternative to Anchor Points When Learning with Noisy Labelscited by 65
  14. 2020Robust regression with covariate filtering: Heavy tails and adversarial contaminationcited by 49
  15. 2021Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Seriescited by 30
  16. 2021Understanding Instance-Level Label Noise: Disparate Impacts and Treatmentscited by 22
  17. 2022Robust Probabilistic Time Series Forecastingcited by 15
  18. 2022Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Seriescited by 14
  19. 2022Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Modelscited by 6
  20. 2022Resilient Neural Forecasting Systemscited by 5
  21. 2022Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Datacited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 8.

17 papers.

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  1. 2024Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspectivecited by 13
  2. 2025Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Modelscited by 8
  3. 2025Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Modelscited by 7
  4. 2024Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecastingcited by 6
  5. 2024Task-oriented Time Series Imputation Evaluation via Generalized Representerscited by 6
  6. 2026DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecastingcited by 3
  7. 2026Diversified Scaling Inference in Time Series Foundation Modelscited by 2
  8. 2025Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modelingcited by 1
  9. 2025Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systemscited by 1
  10. 2025CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Valuescited by 1
  11. 2026SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacementcited by 1
  12. 2025Contrastive Time Series Forecasting with Anomaliescited by 0
5 more
  1. 2026Policy-Controlled Generalized Share: A General Framework with a Transformer Instantiation for Strictly Online Switching-Oracle Trackingcited by 0
  2. 2026MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defensecited by 0
  3. 2026TS-Fault: Benchmarking Time Series Forecasters Against Structural Faultscited by 0
  4. 2026DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modelingcited by 0
  5. 2026Towards Robust Time Series Learning via Capacity-Centric Modulationcited by 0