Ti-MAE: Self-Supervised Masked Time Series Autoencoders

21 January 2023

Papers citing "Ti-MAE: Self-Supervised Masked Time Series Autoencoders"

5 / 5 papers shown

Title
Towards Generalisable Time Series Understanding Across Domains Özgün Turgut Philip Muller M. Menten Daniel Rueckert AI4TS 36 1 0 09 Oct 2024
A Unified Masked Autoencoder with Patchified Skeletons for Motion Synthesis Esteve Valls Mascaro Hyemin Ahn Dongheui Lee CVBM 29 4 0 14 Aug 2023
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 111 391 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 258 7,337 0 11 Nov 2021
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 167 3,799 0 14 Dec 2020