mlsanityData to 14 Oct 2026

From Noise to Laws: Regularized Time-Series Forecasting via Denoised Dynamic Graphs

arXiv 2510.17817 ↗First posted 27 Sep 2025cs.LGcited by 0

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Abstract (arXiv)

Long-horizon multivariate time-series forecasting is challenging because realistic predictions must (i) denoise heterogeneous signals, (ii) track time-varying cross-series dependencies, and (iii) remain stable and physically plausible over long rollout horizons. We present PRISM, which couples a score-based diffusion preconditioner with a dynamic, correlation-thresholded graph encoder and a forecast head regularized by generic physics penalties. We prove contraction of the induced horizon dynamics under mild conditions and derive Lipschitz bounds for graph blocks, explaining the model's robustness. On six standard benchmarks , PRISM achieves consistent SOTA with strong MSE and MAE gains.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 27 Sep 2025.

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21 of 34 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  5. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  6. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  7. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  8. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  9. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  10. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  11. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  12. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
9 more
  1. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  2. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  3. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
  4. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  5. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  6. 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictioncited by 245
  7. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 160
  8. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
  9. 2018Learning graphs from data: A signal representation perspectivecited by 119

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