Results 141 to 150 of about 223,849 (180)

A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective

arXiv.org
Multivariate Time Series Forecasting (MTSF) plays a crucial role across diverse fields, ranging from economic, energy, to traffic. In recent years, deep learning has demonstrated outstanding performance in MTSF tasks.
Xiangfei Qiu, Hanyin Cheng, Xingjian Wu, Jilin Hu, Chenjuan Guo   +4 more
semanticscholar   +1 more source

Temporal Query Network for Efficient Multivariate Time Series Forecasting

International Conference on Machine Learning
Sufficiently modeling the correlations among variables (aka channels) is crucial for achieving accurate multivariate time series forecasting (MTSF).
Shengsheng Lin, Haojun Chen, Haijie Wu, Chunyu Qiu, Weiwei Lin   +4 more
semanticscholar   +1 more source

GinAR+: A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values

IEEE Transactions on Knowledge and Data Engineering
Spatial-Temporal Graph Neural Networks (STGNNs) have been widely utilized in multivariate time series forecasting (MTSF), but they rely on the assumption of data completeness.
Chengqing Yu, Fei Wang, Zezhi Shao, Tangwen Qian, Zhao Zhang, Wei Wei, Zhulin An, Qi Wang, Yongjun Xu   +8 more
semanticscholar   +1 more source

Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation

IEEE International Conference on Data Engineering
Multivariate time series forecasting (MTSF) endeavors to predict future observations given historical data, playing a crucial role in time series data management systems.
Chenxi Liu, Hao Miao, Qianxiong Xu, Shaowen Zhou, Cheng Long, Yan Zhao, Ziyue Li, Rui Zhao   +7 more
semanticscholar   +1 more source

FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting

International Joint Conference on Artificial Intelligence
This paper presents FreEformer, a simple yet effective model that leverages a Frequency Enhanced Transformer for multivariate time series forecasting. Our work is based on the assumption that the frequency spectrum provides a global perspective on the ...
Wenzhen Yue, Yong Liu, Xianghua Ying, Bo Xing, Ruohao Guo, Ji Shi   +5 more
semanticscholar   +1 more source

SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion

Neural Information Processing Systems
Multivariate time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare. Recent studies have highlighted the advantages of channel independence to resist distribution drift but neglect ...
Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan   +3 more
semanticscholar   +1 more source

TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data

AAAI Conference on Artificial Intelligence
The diversity of time series applications and scarcity of domain-specific data highlight the need for time-series models with strong few-shot learning capabilities.
Ege Onur Taga, M. E. Ildiz, Samet Oymak
semanticscholar   +1 more source

DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting

Knowledge Discovery and Data Mining
Multivariate time series forecasting is crucial for various applications, such as financial investment, energy management, weather forecasting, and traffic optimization. However, accurate forecasting is challenging due to two main factors.
Xiangfei Qiu, Xingjian Wu, Yan Lin, Chenjuan Guo, Jilin Hu, Bin Yang   +5 more
semanticscholar   +1 more source

Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling

International Conference on Machine Learning
Predicting multivariate time series is crucial, demanding precise modeling of intricate patterns, including inter-series dependencies and intra-series variations. Distinctive trend characteristics in each time series pose challenges, and existing methods,
Guoqi Yu, Jing Zou, Xiaowei Hu, Angelica I. Avilés-Rivero, Jing Qin, Shujun Wang   +5 more
semanticscholar   +1 more source

HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting

International Conference on Machine Learning
Irregular multivariate time series (IMTS) are characterized by irregular time intervals within variables and unaligned observations across variables, posing challenges in learning temporal and variable dependencies.
Boyuan Li, Yicheng Luo, Zhen Liu, Junhao Zheng, Jianming Lv, Qianli Ma   +5 more
semanticscholar   +1 more source