Disentangling Structured Components: Towards Adaptive, Interpretable and
Scalable Time Series Forecasting

Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting

22 May 2023

Xiusi Chen

Ivor W. Tsang

Papers citing "Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting"

6 / 6 papers shown

Title
Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering Yiming Niu Jinliang Deng L. Zhang Zimu Zhou Yongxin Tong AI4TS 21 0 0 09 May 2025
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis Zezhi Shao Fei Wang Yongjun Xu Wei Wei Chengqing Yu ... Guangyin Jin Xin Cao Gao Cong Christian S. Jensen Xueqi Cheng AI4TS 18 56 0 09 Oct 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
Graph Neural Controlled Differential Equations for Traffic Forecasting Jeongwhan Choi Hwangyong Choi JeeHyun Hwang Noseong Park BDL AI4TS 85 262 1 07 Dec 2021
Discrete Graph Structure Learning for Forecasting Multiple Time Series Chao Shang Jie Chen J. Bi CML BDL AI4TS 92 226 0 18 Jan 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