mlsanityData to 7 Oct 2026

Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting

arXiv 2401.11929 ↗First posted 22 Jan 2024cs.LGcited by 18

Not on the map. Why?

Abstract (arXiv)

Long-term time series forecasting (LTSF) represents a critical frontier in time series analysis, characterized by extensive input sequences, as opposed to the shorter spans typical of traditional approaches. While longer sequences inherently offer richer information for enhanced predictive precision, prevailing studies often respond by escalating model complexity. These intricate models can inflate into millions of parameters, resulting in prohibitive parameter scales. Our study demonstrates, through both analytical and empirical evidence, that decomposition is key to containing excessive model inflation while achieving uniformly superior and robust results across various datasets. Remarkably, by tailoring decomposition to the intrinsic dynamics of time series data, our proposed model outperforms existing benchmarks, using over 99 % fewer parameters than the majority of competing methods. Through this work, we aim to unleash the power of a restricted set of parameters by capitalizing on domain characteristics--a timely reminder that in the realm of LTSF, bigger is not invariably better.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 34 of its 50 references that are in the map. First posted on arXiv 22 Jan 2024.

Builds on

34 of 50 references.

References · 34

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  4. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  5. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  6. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  7. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  8. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  9. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  10. 2021Transformers in Vision: A Surveycited by 1,009
  11. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  12. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
22 more
  1. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  2. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  3. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  4. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  5. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  6. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  7. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  8. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  9. 2023FITS: Modeling Time Series with 10k Parameterscited by 185
  10. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 170
  11. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 149
  12. 2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
  13. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 100
  14. 2023Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecastingcited by 91
  15. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  16. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecastingcited by 86
  17. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  18. 2023DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Modelcited by 64
  19. 2022Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Versioncited by 59
  20. 2022DEPTS: Deep Expansion Learning for Periodic Time Series Forecastingcited by 28
  21. 2023Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Seriescited by 28
  22. 2023Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecastingcited by 19

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 12; 2026: 4.

18 papers.

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  1. 2025Temporal Query Network for Efficient Multivariate Time Series Forecastingcited by 29
  2. 2025Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Ratescited by 19
  3. 2025BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Modelscited by 16
  4. 2025FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidancecited by 9
  5. 2025Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecastingcited by 8
  6. 2024On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecastingcited by 7
  7. 2025PhaseFormer: From Patches to Phases for Efficient and Effective Time Series Forecastingcited by 7
  8. 2025KANMixer: a minimal KAN-centered mixer for long-term time series forecastingcited by 3
  9. 2024Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learnedcited by 2
  10. 2025A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Predictioncited by 2
  11. 2025Non-collective Calibrating Strategy for Time Series Forecastingcited by 1
  12. 2025Inner-Instance Normalization for Time Series Forecastingcited by 1
6 more
  1. 2026PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecastingcited by 1
  2. 2026PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecastingcited by 1
  3. 2025Accurate and Efficient Multivariate Time Series Forecasting via Offline Clusteringcited by 0
  4. 2025ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasterscited by 0
  5. 2026Adaptive Oscillatory-State Alignment for Time Series Forecastingcited by 0
  6. 2026DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecastingcited by 0