mlsanityData to 14 Oct 2026

Treating Brain-inspired Memories as Priors for Diffusion Model to Forecast Multivariate Time Series

arXiv 2409.18491 ↗First posted 27 Sep 2024cs.LGcited by 1

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Forecasting Multivariate Time Series (MTS) involves significant challenges in various application domains. One immediate challenge is modeling temporal patterns with the finite length of the input. These temporal patterns usually involve periodic and sudden events that recur across different channels. To better capture temporal patterns, we get inspiration from humans' memory mechanisms and propose a channel-shared, brain-inspired memory module for MTS. Specifically, brain-inspired memory comprises semantic and episodic memory, where the former is used to capture general patterns, such as periodic events, and the latter is employed to capture special patterns, such as sudden events, respectively. Meanwhile, we design corresponding recall and update mechanisms to better utilize these patterns. Furthermore, acknowledging the capacity of diffusion models to leverage memory as a prior, we present a brain-inspired memory-augmented diffusion model. This innovative model retrieves relevant memories for different channels, utilizing them as distinct priors for MTS predictions. This incorporation significantly enhances the accuracy and robustness of predictions. Experimental results on eight datasets consistently validate the superiority of our approach in capturing and leveraging diverse recurrent temporal patterns across different channels.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 29 of its 43 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

29 of 43 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Non-autoregressive Conditional Diffusion Models for Time Series Prediction8 Jun
  2. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation7 Jul
  3. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting28 Jan

Other references · 26

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  5. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  6. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  7. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  8. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  9. 2020Linformer: Self-Attention with Linear Complexitycited by 1,474
  10. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  11. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  12. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
14 more
  1. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  2. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  3. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  4. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
  5. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  6. 2016RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanismcited by 349
  7. 2016Unsupervised Learning for Physical Interaction through Video Predictioncited by 260
  8. 2022Recurrent Memory Transformercited by 203
  9. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 198
  10. 2015Action-Conditional Video Prediction using Deep Networks in Atari Gamescited by 172
  11. 2022CARD: Classification and Regression Diffusion Modelscited by 103
  12. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  13. 2019Generalization of Reinforcement Learners with Working and Episodic Memorycited by 36
  14. 2024Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecastingcited by 4

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Diffusion Models for Time Series Forecasting: A Surveycited by 11

Similar, not cited

Close in wording, no citation either way

  1. 2026Enhancing few-shot time series forecasting with LLM-guided diffusion
  2. 2026Memory in Deep Time-Series Models
  3. 2026ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series
  4. 2026MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
  5. 2025Multimodal Conditioned Diffusive Time Series Forecasting
  6. 2023Diffusion Models for Time Series Applications: A Survey
  7. 2023Interacting Diffusion Processes for Event Sequence Forecasting
  8. 2024CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding