mlsanityData to 7 Oct 2026

CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns

arXiv 2409.18479 ↗First posted 27 Sep 2024cs.LGcited by 102

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Abstract (arXiv)

The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly modeling this periodicity to enhance the performance of models in long-term time series forecasting (LTSF) tasks. Specifically, we introduce the Residual Cycle Forecasting (RCF) technique, which utilizes learnable recurrent cycles to model the inherent periodic patterns within sequences, and then performs predictions on the residual components of the modeled cycles. Combining RCF with a Linear layer or a shallow MLP forms the simple yet powerful method proposed in this paper, called CycleNet. CycleNet achieves state-of-the-art prediction accuracy in multiple domains including electricity, weather, and energy, while offering significant efficiency advantages by reducing over 90% of the required parameter quantity. Furthermore, as a novel plug-and-play technique, the RCF can also significantly improve the prediction accuracy of existing models, including PatchTST and iTransformer. The source code is available at: https://github.com/ACAT-SCUT/CycleNet.

Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). Builds on 47 of its 60 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

47 of 60 references.

References · 47

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  5. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  6. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  7. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  8. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  9. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  10. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  11. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,187
  12. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
35 more
  1. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  2. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  3. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  4. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  5. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  6. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  7. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  8. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  9. 2022Transformers in Time Series: A Surveycited by 489
  10. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  11. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
  12. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 333
  13. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 327
  14. 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 267
  15. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 242
  16. 2023FITS: Modeling Time Series with 10k Parameterscited by 185
  17. 2022PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecastingcited by 170
  18. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 170
  19. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 158
  20. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 146
  21. 2024SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameterscited by 103
  22. 2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 100
  23. 2024SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusioncited by 75
  24. 2023Ti-MAE: Self-Supervised Masked Time Series Autoencoderscited by 63
  25. 2024LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Promptingcited by 56
  26. 2023SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecastingcited by 53
  27. 2023MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixingcited by 52
  28. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  29. 2024Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modelingcited by 44
  30. 2024An Analysis of Linear Time Series Forecasting Modelscited by 40
  31. 2022DEPTS: Deep Expansion Learning for Periodic Time Series Forecastingcited by 28
  32. 2023PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformercited by 22
  33. 2024RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Taskscited by 22
  34. 2023PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecastingcited by 20
  35. 2023SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecastingcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 52; 2026: 45.

102 papers.

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  1. 2024DUET: Dual Clustering Enhanced Multivariate Time Series Forecastingcited by 104
  2. 2025A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspectivecited by 58
  3. 2025Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baselinecited by 54
  4. 2025DBLoss: Decomposition-based Loss Function for Time Series Forecastingcited by 44
  5. 2024Fourier Position Embedding: Enhancing Attention's Periodic Extension for Length Generalizationcited by 37
  6. 2025Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspectivecited by 34
  7. 2025Temporal Query Network for Efficient Multivariate Time Series Forecastingcited by 29
  8. 2025LightGTS: A Lightweight General Time Series Forecasting Modelcited by 28
  9. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 28
  10. 2024xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memoriescited by 18
  11. 2025DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignmentcited by 13
  12. 2025TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecastingcited by 11
38 more
  1. 2025Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Modelscited by 10
  2. 2025CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlationscited by 8
  3. 2025Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecastingcited by 8
  4. 2025K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecastingcited by 7
  5. 2024How Much Can Time-related Features Enhance Time Series Forecasting?cited by 6
  6. 2025Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?cited by 6
  7. 2025CC-Time: Cross-Model and Cross-Modality Time Series Forecastingcited by 6
  8. 2025LETS Forecast: Learning Embedology for Time Series Forecastingcited by 5
  9. 2025WSSM: Geographic-enhanced hierarchical state-space model for global station weather forecastcited by 4
  10. 2025Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecastingcited by 4
  11. 2025Multi-Order Wavelet Derivative Transform for Deep Time Series Forecastingcited by 4
  12. 2025Augur: Modeling Covariate Causal Associations in Time Series via Large Language Modelscited by 3
  13. 2026TimeSqueeze: Dynamic Patching for Efficient Time Series Forecastingcited by 3
  14. 2025TGT: A Temporal Gating Transformer for Smartphone App Usage Predictioncited by 2
  15. 2025Semantic-Enhanced Time-Series Forecasting via Large Language Modelscited by 2
  16. 2025ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecastingcited by 2
  17. 2025Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecastingcited by 2
  18. 2025Tsururu: A Python-based Time Series Forecasting Strategies Librarycited by 2
  19. 2025Task-Aware Mixture-of-Experts for Time Series Analysiscited by 2
  20. 2025ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecastingcited by 2
  21. 2025SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasterscited by 2
  22. 2025Time Series Forecasting via Direct Per-Step Probability Distribution Modelingcited by 2
  23. 2026PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecastingcited by 2
  24. 2026Deep Time-series Forecasting Needs Kernelized Moment Balancingcited by 2
  25. 2026Moving Beyond Functional Connectivity: Time-Series Modeling for fMRI-Based Brain Disorder Classificationcited by 2
  26. 2026Enhancing Multivariate Time Series Forecasting with Global Temporal Retrievalcited by 2
  27. 2026Stationarity-Aware Retrieval-Augmented Time Series Forecastingcited by 2
  28. 2026Semantics-Enhanced Retrieval-Augmented Time Series Forecastingcited by 2
  29. 2024AverageTime: Enhance Long-Term Time Series Forecasting with Simple Averagingcited by 1
  30. 2025MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecastingcited by 1
  31. 2025Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecastingcited by 1
  32. 2025A Dynamic Stiefel Graph Neural Network for Efficient Spatio-Temporal Time Series Forecastingcited by 1
  33. 2025Uncovering Insights of Compound Flooding with Data-Driven AIcited by 1
  34. 2025MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecastingcited by 1
  35. 2025ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecastingcited by 1
  36. 2025Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecastingcited by 1
  37. 2025ReCast: Reliability-aware Codebook Assisted Lightweight Time Series Forecastingcited by 1
  38. 2026FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Expertscited by 1

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