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.
翻译:时间序列预测是一个长期的挑战, 原因是真实世界的信息在不同的情景中存在( 能源、 天气、 交通、 经济学、 地震警报 ) 。 但是, 一些主流预测模型预测结果从地面真相中急剧脱轨。 我们认为, 原因是模型缺乏获取频率信息的能力, 而在真实世界数据集中大量含有频率信息。 目前, 主流频率信息提取方法基于 Fourier 变换( Freier 变换( FT) 。 然而, 由于 Gibbs 现象, 使用FT 有问题。 如果序列两侧的数值差异很大, 则在两侧都观测到悬浮近似和高频噪音。 因此, 我们提议增加频道的频率, 以适应性模式建模基于混乱的 Cosine 变换的频道之间的频率相互依存关系。 这在Fourier 变换( 被定义为 Gibbs Phenomon) 。 然而, 这个网络在六个真实世界数据集中非常有效, 我们进一步显示频道的频率增加的频率, 10. 01 和高频直线, 在现有的M- 流流化网络上, 在不同的网络上可以灵活地应用到 80.