Multivariate Time Series Forecasting

Multivariate time series forecasting aims to predict the future values of multiple interconnected time series, a crucial task across diverse fields like finance and healthcare. Current research emphasizes improving model accuracy and robustness, focusing on architectures like Transformers and Graph Neural Networks, as well as addressing challenges such as non-stationarity, missing data, and computational efficiency through techniques like frequency domain analysis and lightweight model designs. These advancements are significant for improving decision-making in various applications by providing more accurate and reliable predictions, particularly in scenarios with high-dimensional data and complex temporal dependencies.

Papers

September 26, 2023

Revealing the Power of Spatial-Temporal Masked Autoencoders in Multivariate Time Series Forecasting
Jiarui Sun, Yujie Fan, Chin-Chia Michael Yeh, Wei Zhang, Girish Chowdhary
Real Power Spatio Temporal Multivariate Time Series Forecasting Masked Autoencoder

August 23, 2023

Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting
Yifan Zhang, Rui Wu, Sergiu M. Dascalu, Frederick C. Harris
Multi Scale Multivariate Time Series Temporal Dependency Multivariate Time Series Forecasting

August 7, 2023

DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction
Chengqing Yu, Fei Wang, Zezhi Shao, Tao Sun, Lin Wu, Yongjun Xu
Transformer Based Model Multivariate Time Series Temporal Attention Multivariate Time Series Forecasting Accurate Predictive Model

July 4, 2023

SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
Zhenwei Zhang, Linghang Meng, Yuantao Gu
New Framework Multivariate Time Series Forecasting Graph Aware Transformer Inter Series

June 30, 2023

Improving the Transferability of Time Series Forecasting with Decomposition Adaptation
Yan Gao, Yan Wang, Qiang Wang
Time Series Forecasting Task Transferability Multivariate Time Series Forecasting Joint Distribution Adaptation Neural Forecasting Decomposition Network

June 15, 2023

Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Model
Zahra Fatemi, Minh Huynh, Elena Zheleva, Zamir Syed, Xiaojun Di
Multivariate Time Series Multivariate Time Series Forecasting Multivariate Time Series Data Causal Prediction Warm Start CDF Cold Framework

June 14, 2023

June 12, 2023

MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting
Xing Wang, Zhendong Wang, Kexin Yang, Junlan Feng, Zhiyan Song, Chao Deng, Lin zhu
Time Series Forecasting Multivariate Time Series Forecasting Long Term Time Series Forecasting Pattern Mining

June 4, 2023

Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting
Donghao Luo, Xue Wang
Time Series Forecasting Multivariate Time Series State of the Art Forecasting Multivariate Time Series Forecasting

May 30, 2023

Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting
Jiaxin Gao, Wenbo Hu, Yuntian Chen
Transformer Based Model Multivariate Time Series Forecasting Long Term Time Series Forecasting Client Collaboration

May 25, 2023

Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting
Zheng Sun, Yi Wei, Wenxiao Jia, Long Yu
Transformer Based Model Spatio Temporal Multivariate Time Series Forecasting Temporal Transformer Long Term Time Series Forecasting

May 24, 2023

A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting
Yushu Chen, Shengzhuo Liu, Jinzhe Yang, Hao Jing, Wenlai Zhao, Guangwen Yang
Transformer Model Multivariate Time Series Forecasting Frequency Domain Representation Joint Transformer

May 22, 2023

Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang, Du Yin, Yi Yang, Xuan Song, Ivor W. Tsang
Adaptive Importance Multivariate Time Series Multivariate Time Series Forecasting Structural Engineering Forecasting System

April 11, 2023

The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting
Lu Han, Han-Jia Ye, De-Chuan Zhan
Multivariate Time Series Multivariate Time Series Forecasting Multivariate Time Series Data Forecasting Model Capacity Loss Multivariate Forecasting Robustness Trade

February 11, 2023

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Fan Zhou, Chen Pan, Lintao Ma, Yu Liu, Shiyu Wang, James Zhang, Xinxin Zhu, Xuanwei Hu, Yunhua Hu, Yangfei Zheng, Lei Lei, Yun Hu
Time Series Forecasting Multivariate Time Series Forecasting Part Whole Hierarchy Forecasting Performance Hierarchical Information Hierarchical Forecasting Hierarchical Time Series Learning Structure Single Population Optimization Task

February 9, 2023

MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
Zhe Li, Zhongwen Rao, Lujia Pan, Zenglin Xu
Transformer Based Model Time Series Forecasting Temporal Information Attention Module Multivariate Time Series Forecasting Forecasting Task Simple Alternating Mixer Channel Mixing

January 21, 2023

Ti-MAE: Self-Supervised Masked Time Series Autoencoders
Zhe Li, Zhongwen Rao, Lujia Pan, Pengyun Wang, Zenglin Xu
Time Series Forecasting Multivariate Time Series Forecasting Masked AutoEncoder Sce Mae Transformer Based Generative Input Time Series

December 28, 2022

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Shiyu Wang, Fan Zhou, Yinbo Sun, Lintao Ma, James Zhang, Yangfei Zheng, Bo Zheng, Lei Lei, Yun Hu
End to End Multivariate Time Series Forecasting Autoregressive Transformer Conditional Normalizing Flow Hierarchical Datasets Hierarchical Time Series Hierarchical Time Series Forecasting Nonlinear Correlation

December 20, 2022

A Pattern Discovery Approach to Multivariate Time Series Forecasting
Yunyao Cheng, Chenjuan Guo, Kaixuan Chen, Kai Zhao, Bin Yang, Jiandong Xie, Christian S. Jensen, Feiteng Huang, Kai Zheng
Multivariate Time Series Forecasting Complex Time Series Pattern Discovery Correlation Learning Nonlinear Correlation

Multivariate Time Series Forecasting

Papers

Revealing the Power of Spatial-Temporal Masked Autoencoders in Multivariate Time Series Forecasting

Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting

DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction

SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting

Improving the Transferability of Time Series Forecasting with Decomposition Adaptation

Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Model

GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting

TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting

MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting

Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting

Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting

Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting

A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting

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

The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing

Ti-MAE: Self-Supervised Masked Time Series Autoencoders

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

A Pattern Discovery Approach to Multivariate Time Series Forecasting