# FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting

arXiv: https://arxiv.org/abs/2212.01209 · HTML: https://mlsanity.com/paper/2212.01209/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 78 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://mlsanity.com/paper/2106.09305/) (2021)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [Multiwavelet-based Operator Learning for Differential Equations](https://mlsanity.com/paper/2109.13459/) (2021)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting](https://mlsanity.com/paper/2205.08897/) (2022)
- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://mlsanity.com/paper/2201.12886/) (2022)
- [Risk-Averse Trust Region Optimization for Reward-Volatility Reduction](https://mlsanity.com/paper/1912.03193/) (2019)
- [Long-term Forecasting using Higher Order Tensor RNNs](https://mlsanity.com/paper/1711.00073/) (2017)
- [Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series](https://mlsanity.com/paper/2103.02164/) (2021)
- [Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks](https://mlsanity.com/paper/1712.08083/) (2017)
- [Forecasting the onset and course of mental illness with Twitter data](https://mlsanity.com/paper/1608.07740/) (2016)

## Built on by (most cited first, 15 of 15)

- [A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective](https://mlsanity.com/paper/2502.10721/) (2025)
- [CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2406.05316/) (2024)
- [ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2502.16890/) (2025)
- [CAFO: Feature-Centric Explanation on Time Series Classification](https://mlsanity.com/paper/2406.01833/) (2024)
- [WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2410.22649/) (2024)
- [Adversarial Causal Tuning for Realistic Time-series Generation](https://mlsanity.com/paper/2506.02084/) (2025)
- [Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network](https://mlsanity.com/paper/2412.19842/) (2024)
- [FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting](https://mlsanity.com/paper/2405.13300/) (2024)
- [RoWSFormer: A Robust Watermarking Framework with Swin Transformer for Enhanced Geometric Attack Resilience](https://mlsanity.com/paper/2409.14829/) (2024)
- [Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation](https://mlsanity.com/paper/2506.01327/) (2025)
- [Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion](https://mlsanity.com/paper/2506.07099/) (2025)
- [Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services](https://mlsanity.com/paper/2507.12908/) (2025)
- [WindowMixer: Intra-Window and Inter-Window Modeling for Time Series Forecasting](https://mlsanity.com/paper/2406.12921/) (2024)
- [Fast-Powerformer: A Memory-Efficient Transformer for Accurate Mid-Term Wind Power Forecasting](https://mlsanity.com/paper/2504.10923/) (2025)
- [GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework](https://mlsanity.com/paper/2512.00406/) (2025)

## Abstract (arXiv)

Time series forecasting is a long-standing challenge due to the real-world information is in various scenario (e.g., energy, weather, traffic, economics, earthquake warning). However some mainstream forecasting model forecasting result is derailed dramatically from ground truth. We believe it's the reason that model's lacking ability of capturing frequency information which richly contains in real world datasets. At present, the mainstream frequency information extraction methods are Fourier transform(FT) based. However, use of FT is problematic due to Gibbs phenomenon. If the values on both sides of sequences differ significantly, oscillatory approximations are observed around both sides and high frequency noise will be introduced. Therefore We propose a novel frequency enhanced channel attention that adaptively modelling frequency interdependencies between channels based on Discrete Cosine Transform which would intrinsically avoid high frequency noise caused by problematic periodity during Fourier Transform, which is defined as Gibbs Phenomenon. We show that this network generalize extremely effectively across six real-world datasets and achieve state-of-the-art performance, we further demonstrate that frequency enhanced channel attention mechanism module can be flexibly applied to different networks. This module can improve the prediction ability of existing mainstream networks, which reduces 35.99% MSE on LSTM, 10.01% on Reformer, 8.71% on Informer, 8.29% on Autoformer, 8.06% on Transformer, etc., at a slight computational cost ,with just a few line of code. Our codes and data are available at https://github.com/Zero-coder/FECAM.