mlsanityData to 7 Oct 2026

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective

arXiv 2402.11463 ↗First posted 18 Feb 2024cs.LG cs.AI nlin.CDcited by 17

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Abstract (arXiv)

In long-term time series forecasting (LTSF) tasks, an increasing number of models have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their dynamical structures. Recognizing the chaotic nature of real-world data, our model, textbftextitAttraos, incorporates chaos theory into LTSF, perceiving real-world time series as observations from unknown high-dimensional chaotic dynamic systems. Under the concept of attractor invariance, Attraos utilizes non-parametric Phase Space Reconstruction embedding and the proposed multi-scale dynamic memory unit to memorize historical dynamics structure and predicts by a frequency-enhanced local evolution strategy. Detailed theoretical analysis and abundant empirical evidence consistently show that Attraos outperforms various LTSF methods on mainstream LTSF datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 27 of its 68 references that are in the map. First posted on arXiv 18 Feb 2024.

Builds on

27 of 68 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  3. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  4. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  5. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  6. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  7. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  8. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  9. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  10. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  11. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  12. 2022Simplified State Space Layers for Sequence Modelingcited by 776
15 more
  1. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  2. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 600
  3. 2020Hopfield Networks is All You Needcited by 461
  4. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
  5. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  6. 2021Multiwavelet-based Operator Learning for Differential Equationscited by 229
  7. 2022FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecastingcited by 147
  8. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 139
  9. 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictorscited by 110
  10. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  11. 2023Generalized Teacher Forcing for Learning Chaotic Dynamicscited by 55
  12. 2023SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecastingcited by 53
  13. 2022Koopman Neural Forecaster for Time Series with Temporal Distribution Shiftscited by 35
  14. 2024RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Taskscited by 22
  15. 2023Sequence Modeling with Multiresolution Convolutional Memorycited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 6; 2026: 6.

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  1. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 280
  2. 2025Kimi Linear: An Expressive, Efficient Attention Architecturecited by 169
  3. 2024DUET: Dual Clustering Enhanced Multivariate Time Series Forecastingcited by 104
  4. 2024Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecastingcited by 9
  5. 2025Vision-Enhanced Time Series Forecasting via Latent Diffusion Modelscited by 9
  6. 2025LETS Forecast: Learning Embedology for Time Series Forecastingcited by 5
  7. 2025Multi-Order Wavelet Derivative Transform for Deep Time Series Forecastingcited by 4
  8. 2026Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariatescited by 4
  9. 2024Toward Physics-guided Time Series Embeddingcited by 3
  10. 2024TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecastingcited by 1
  11. 2025Time Series Forecasting Through the Lens of Dynamicscited by 1
  12. 2026A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Featurescited by 1
5 more
  1. 2025Parallel BiLSTM-Transformer networks for forecasting chaotic dynamicscited by 0
  2. 2026The Spectrum Is Not Enough: When Context Helps Time-Series Forecastingcited by 0
  3. 2026POEM: Phase-Aware SO(2) Feature Rotation for Time Series Forecasting Under Periodicity Driftcited by 0
  4. 2026Correct then Forecast: Observer State-Space Models for Time Series Forecastingcited by 0
  5. 2026XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matchingcited by 0