mlsanityData to 14 Oct 2026

WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting

arXiv 2309.11319 ↗First posted 20 Sep 2023cs.LGcited by 10

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Abstract (arXiv)

Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourier transform, which cannot capture fine-grained and local frequency structure. In this paper, we propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. WFTNet utilizes both Fourier and wavelet transforms to extract comprehensive temporal-frequency information from the signal, where Fourier transform captures the global periodic patterns and wavelet transform captures the local ones. Furthermore, we introduce a Periodicity-Weighted Coefficient (PWC) to adaptively balance the importance of global and local frequency patterns. Extensive experiments on various time series datasets show that WFTNet consistently outperforms other state-of-the-art baseline. Code is available at https://github.com/Hank0626/WFTNet.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 20 Sep 2023.

Builds on

12 of 22 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  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. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  6. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  7. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  8. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  9. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  10. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 338
  11. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 148
  12. 2022Long Short-Term Memory Neural Network for Financial Time Seriescited by 8

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Cited by year (2026 to 14 Oct)

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

10 papers.

  1. 2024CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuningcited by 108
  2. 2024TSLANet: Rethinking Transformers for Time Series Representation Learningcited by 74
  3. 2025Wavelet Mixture of Experts for Time Series Forecastingcited by 2
  4. 2025IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecastingcited by 1
  5. 2026InstanceRSR: Real-World Super-Resolution via Instance-Aware Representation Alignmentcited by 1
  6. 2025CANet: ChronoAdaptive Network for Enhanced Long-Term Time Series Forecasting under Non-Stationaritycited by 0
  7. 2025PRISM: Lightweight Multivariate Time-Series Classification through Symmetric Multi-Resolution Convolutional Layerscited by 0
  8. 2026ScatterFusion: A Hierarchical Scattering Transform Framework for Enhanced Time Series Forecastingcited by 0
  9. 2026Deep Time-Series Forecasting in 10 Years: A Surveycited by 0
  10. 2026DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecastingcited by 0