mlsanityData to 14 Oct 2026

CATS-Linear: Classification Auxiliary Linear Model for Time Series Forecasting

arXiv 2510.08661 ↗First posted 9 Oct 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Recent research demonstrates that linear models achieve forecasting performance competitive with complex architectures, yet methodologies for enhancing linear models remain underexplored. Motivated by the hypothesis that distinct time series instances may follow heterogeneous linear mappings, we propose the Classification Auxiliary Trend-Seasonal Decoupling Linear Model CATS-Linear, employing Classification Auxiliary Channel-Independence (CACI). CACI dynamically routes instances to dedicated predictors via classification, enabling supervised channel design. We further analyze the theoretical expected risks of different channel settings. Additionally, we redesign the trend-seasonal decomposition architecture by adding a decoupling -- linear mapping -- recoupling framework for trend components and complex-domain linear projections for seasonal components. Extensive experiments validate that CATS-Linear with fixed hyperparameters achieves state-of-the-art accuracy comparable to hyperparameter-tuned baselines while delivering SOTA accuracy against fixed-hyperparameter counterparts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 46 references that are in the map. First posted on arXiv 9 Oct 2025.

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References · 29

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  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. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  7. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  8. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  9. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
  10. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  11. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 332
  12. 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 269
17 more
  1. 2023FITS: Modeling Time Series with 10k Parameterscited by 195
  2. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 168
  3. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 148
  4. 2023TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecastingcited by 139
  5. 2017Conditional Time Series Forecasting with Convolutional Neural Networkscited by 123
  6. 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 116
  7. 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictorscited by 113
  8. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 101
  9. 2023CARD: Channel Aligned Robust Blend Transformer for Time Series Forecastingcited by 55
  10. 2023MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixingcited by 53
  11. 2024FilterNet: Harnessing Frequency Filters for Time Series Forecastingcited by 47
  12. 2024An Analysis of Linear Time Series Forecasting Modelscited by 41
  13. 2024From Similarity to Superiority: Channel Clustering for Time Series Forecastingcited by 37
  14. 2025OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domaincited by 26
  15. 2024Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecastingcited by 8
  16. 2024A Temporal Linear Network for Time Series Forecastingcited by 4
  17. 2025Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Predictioncited by 4

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