Frequency-domain MLPs are More Effective Learners in Time Series
Forecasting

Frequency-domain MLPs are More Effective Learners in Time Series Forecasting

10 November 2023

Defu Lian

Papers citing "Frequency-domain MLPs are More Effective Learners in Time Series Forecasting"

7 / 7 papers shown

Title
FLARE: A Framework for Stellar Flare Forecasting using Stellar Physical Properties and Historical Records Bingke Zhu Xiaoxiao Wang Minghui Jia Yihan Tao Xiao Kong Ali Luo Yingying Chen Ming Tang J. T. Wang 38 42 0 25 Feb 2025
FreDF: Learning to Forecast in the Frequency Domain Hao Wang Licheng Pan Zhichao Chen Degui Yang Sen Zhang Yifei Yang Xinggao Liu Haoxuan Li Dacheng Tao AI4TS 24 46 0 04 Feb 2024
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures T. Zhang Yizhuo Zhang Wei Cao Jiang Bian Xiaohan Yi Shun Zheng Jian Li BDL AI4TS 74 91 0 04 Jul 2022
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting Tian Zhou Ziqing Ma Xue Wang Qingsong Wen Liang Sun Tao Yao Wotao Yin Rong Jin AI4TS 103 102 0 18 May 2022
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting Gerald Woo Chenghao Liu Doyen Sahoo Akshat Kumar Steven C. H. Hoi AI4TS 93 352 0 03 Feb 2022
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting Haoyi Zhou Shanghang Zhang J. Peng Shuai Zhang Jianxin Li Hui Xiong Wan Zhang AI4TS 146 2,327 0 14 Dec 2020
Efficient Estimation of Word Representations in Vector Space Tomáš Mikolov Kai Chen G. Corrado J. Dean 3DV 226 9,999 0 16 Jan 2013