MLSP-L7.5
DECOMPOSED SEASONAL-TREND NETWORK WITH ROTARY ATTENTION FOR TIME SERIES FORECASTING
Tao Sun, Sijie Xiong, Cheng Tang, Kyushu University, Japan; Haichuan Yang, Tokushima University, Japan; Fumiya Okubo, Atsushi Shimada, Kyushu University, Japan
Session:
MLSP-L7: Deep Learning for Structured and Temporal Signals Oral
Track:
Machine Learning for Signal Processing [ML]
Location:
Room 120+121
Presentation Time:
Tue, 5 May, 17:50 - 18:10
Presentation
Discussion
Resources
No resources available.
Session MLSP-L7
MLSP-L7.1: INFORMATION-PRESERVING DOWNSAMPLING AND BIDIRECTIONAL FUSION FOR MULTI-SCALE TIME SERIES FORECASTING
Yunxiang Song, Shuguo Hu, Huaiwen Zhang, Inner Mongolia University, China
MLSP-L7.2: SARNET: A SPIKE-AWARE CONSECUTIVE VALIDATION FRAMEWORK FOR ACCURATE REMAINING USEFUL LIFE PREDICTION
Junhao Fan, Georgetown University, United States of America; Wenrui Liang, Wei-Qiang Zhang, Tsinghua University, China
MLSP-L7.3: SCAF: SOFT CLUSTER-AWARE FUSION WITH AFFINITY ALIGNMENT FOR MULTIVARIATE TIME SERIES FORECASTING
Xiao Liu, Weimin Li, Shanghai University, China; Zhongming Han, Beijing Technology and Business University, China; Ruiqiang Guo, Hebei Normal University, China
MLSP-L7.4: KAN-LLM: Kolmogorov-Arnold Networks-enhanced Large Language Models For Time Series Forecasting
Kang Li, Xiao Zhang, Junqi Liu, Tianyu Hu, Huimin Ma, University of Science and Technology Beijing, China
MLSP-L7.5: DECOMPOSED SEASONAL-TREND NETWORK WITH ROTARY ATTENTION FOR TIME SERIES FORECASTING
Tao Sun, Sijie Xiong, Cheng Tang, Kyushu University, Japan; Haichuan Yang, Tokushima University, Japan; Fumiya Okubo, Atsushi Shimada, Kyushu University, Japan
MLSP-L7.6: MODERN STRUCTURE-AWARE SIMPLICIAL SPATIOTEMPORAL NEURAL NETWORK
Zhaobo Hu, Vincent Gauthier, Institut Polytechnique de Paris, France; Mehdi Naima, Sorbonne Université, France
Contacts