mlsanityData to 7 Oct 2026

DeepFilter: A Transformer-style Framework for Accurate and Efficient Process Monitoring

arXiv 2501.01342 ↗First posted 2 Jan 2025cs.AI cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

The process monitoring task is characterized by stringent demands for accuracy and efficiency. Current transformer-based methods, characterized by self-attention for temporal fusion, exhibit limitations in accurately understanding the semantic context and efficiently processing monitoring logs, rendering them inadequate for process monitoring. To address these limitations, we introduce DeepFilter, which revises the self-attention mechanism to improve both accuracy and efficiency. As a straightforward yet versatile approach, DeepFilter provides an instrumental baseline for practitioners in process monitoring, whether initiating new projects or enhancing existing capabilities.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 27 of its 84 references that are in the map. First posted on arXiv 2 Jan 2025.

Builds on

27 of 84 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  4. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  5. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  6. 2020Rethinking Attention with Performerscited by 1,584
  7. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  8. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  9. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  10. 2022Transformers in Time Series: A Surveycited by 489
  11. 2021Global Filter Networks for Image Classificationcited by 257
  12. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
15 more
  1. 2020Learning in the Frequency Domaincited by 196
  2. 2023FITS: Modeling Time Series with 10k Parameterscited by 185
  3. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 170
  4. 2020FastPitch: Parallel Text-to-speech with Pitch Predictioncited by 150
  5. 2022FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecastingcited by 147
  6. 2022CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecastingcited by 143
  7. 2024DUET: Dual Clustering Enhanced Multivariate Time Series Forecastingcited by 104
  8. 2024CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patchingcited by 91
  9. 2024FreDF: Learning to Forecast in the Frequency Domaincited by 77
  10. 2024Frequency Adaptive Normalization For Non-stationary Time Series Forecastingcited by 45
  11. 2024Fredformer: Frequency Debiased Transformer for Time Series Forecastingcited by 43
  12. 2023A Survey on Deep Learning based Time Series Analysis with Frequency Transformationcited by 40
  13. 2023An Accurate and Interpretable Framework for Trustworthy Process Monitoringcited by 18
  14. 2022Entire Space Counterfactual Learning for Reliable Content Recommendationscited by 13
  15. 2024Not All Frequencies Are Created Equal:Towards a Dynamic Fusion of Frequencies in Time-Series Forecastingcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 28
  2. 2025Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecastingcited by 19
  3. 2026GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variablescited by 11
  4. 2026PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecastingcited by 2
  5. 2026Deep Time-Series Forecasting in 10 Years: A Surveycited by 0