我们研究了频域MLPs学习到的模式,并发现了它们具有的两个固有特性,即(i)全局视角:频谱使MLPs能够全面理解信号并更轻松地学习全局依赖关系;(ii)能量压缩:频域MLPs专注于较小的频率成分,使信号能量更加集中。接着,我们提出了FreTS,这是一种简单而有效的架构,基于频域MLPs进行时间序列预测。FreTS主要包括两个阶段:(i)...
Reconfigurable acceleration of robust frequency-domain 星级: 17 页 Frequency-domain【PPT-课件】 星级: 47 页 Frequency-domain parameter estimation of general multi-rate systems 星级: 12 页 Frequency-Domain Receivers for Rate-1 Space-Time Block Codes 星级: 7 页 Frequency-Domain 星级: 117 页...
Official implementation of the paper "Frequency-domain MLPs are More Effective Learners in Time Series Forecasting" - aikunyi/FreTS
🚩 [NeurIPS 2023]:Frequency-domain MLPs are more effective learners in time series forecasting 🚩 [NeurIPS 2023]:FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective 🚩 [arXiv]:A Survey on Deep Learning based Time Series Analysis with Frequency Transformation...
The global deformation block consists of five shared MLPs of sizes 1024, 512, 256, 128, and 3, with BN layers and ReLU on the first four layers and then activation on the output layer. We concatenate the vertex position V of the mesh G0 as a position encoding with the feature h to ...