SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

2 February 2023

Li Zhang

Papers citing "SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling"

9 / 59 papers shown

Title
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series Chenxi Sun Hongyan Li Yaliang Li linda Qiao AI4TS 21 111 0 16 Aug 2023
PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer Shengsheng Lin Weiwei Lin Wentai Wu Song Wang Yongxiang Wang AI4TS 30 18 0 09 Aug 2023
Unsupervised Representation Learning for Time Series: A Review Qianwen Meng Hangwei Qian Yong Liu Yonghui Xu Zhiqi Shen Li-zhen Cui AI4TS 30 17 0 03 Aug 2023
A Survey on Time-Series Pre-Trained Models Qianli Ma Z. Liu Zhenjing Zheng Ziyang Huang Siying Zhu Zhongzhong Yu James T. Kwok AI4TS 26 50 0 18 May 2023
Revealing the Dark Secrets of Masked Image Modeling Zhenda Xie Zigang Geng Jingcheng Hu Zheng-Wei Zhang Han Hu Yue Cao VLM 186 139 0 26 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 111 394 0 03 Feb 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 302 7,434 0 11 Nov 2021
Making Pre-trained Language Models Better Few-shot Learners Tianyu Gao Adam Fisch Danqi Chen 241 1,918 0 31 Dec 2020
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 169 3,876 0 14 Dec 2020