mlsanityData to 14 Oct 2026

FTS: A Framework to Find a Faithful TimeSieve

arXiv 2405.19647 ↗First posted 30 May 2024cs.LGcited by 7

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Abstract (arXiv)

The field of time series forecasting has garnered significant attention in recent years, prompting the development of advanced models like TimeSieve, which demonstrates impressive performance. However, an analysis reveals certain unfaithfulness issues, including high sensitivity to random seeds and minute input noise perturbations. Recognizing these challenges, we embark on a quest to define the concept of textbfunderlineFaithful underlineTimeunderlineSieve underline(FTS), a model that consistently delivers reliable and robust predictions. To address these issues, we propose a novel framework aimed at identifying and rectifying unfaithfulness in TimeSieve. Our framework is designed to enhance the model's stability and resilience, ensuring that its outputs are less susceptible to the aforementioned factors. Experimentation validates the effectiveness of our proposed framework, demonstrating improved faithfulness in the model's behavior. Looking forward, we plan to expand our experimental scope to further validate and optimize our algorithm, ensuring comprehensive faithfulness across a wide range of scenarios. Ultimately, we aspire to make this framework can be applied to enhance the faithfulness of not just TimeSieve but also other state-of-the-art temporal methods, thereby contributing to the reliability and robustness of temporal modeling as a whole.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 20 of its 45 references that are in the map. First posted on arXiv 30 May 2024.

Builds on

20 of 45 references.

References · 20

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  5. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  6. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  7. 2017Opening the Black Box of Deep Neural Networks via Informationcited by 503
  8. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 391
  9. 2015LSTM: A Search Space Odysseycited by 316
  10. 2017Gate-Variants of Gated Recurrent Unit (GRU) Neural Networkscited by 242
  11. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 172
  12. 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictorscited by 113
8 more
  1. 2020RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networkscited by 55
  2. 2023LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Versioncited by 50
  3. 2023Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistencycited by 42
  4. 2022SEAT: Stable and Explainable Attentioncited by 25
  5. 2024SATO: Stable Text-to-Motion Frameworkcited by 20
  6. 2022Robust Probabilistic Time Series Forecastingcited by 15
  7. 2023A Multi-Scale Decomposition MLP-Mixer for Time Series Analysiscited by 10
  8. 2020Bayesian Median Autoregression for Robust Time Series Forecastingcited by 2

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Cited by year

Citing papers per year: 2024: 4; 2025: 3.

7 papers.

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  3. 2025Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learningcited by 2
  4. 2024CCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolutioncited by 0
  5. 2024Case-based reasoning approach for diagnostic screening of children with developmental delayscited by 0
  6. 2025A High-Performance Thermal Infrared Object Detection Framework with Centralized Regulationcited by 0
  7. 2025Machine Learning Algorithm for Noise Reduction and Disease-Causing Gene Feature Extraction in Gene Sequencing Datacited by 0