Time Series Classification
Time series classification focuses on automatically assigning labels to sequential data, aiming to improve accuracy and efficiency across diverse applications. Current research emphasizes developing robust and efficient models, including those based on random forests, convolutional neural networks (CNNs), transformers, and state-space models, often incorporating techniques like transfer learning, multi-objective optimization, and self-supervised learning to address challenges such as limited data, high dimensionality, and noise. These advancements are significant for various fields, enabling improved accuracy in applications ranging from healthcare diagnostics and financial forecasting to environmental monitoring and industrial process control.
Papers
Stochastic Sparse Sampling: A Framework for Variable-Length Medical Time Series Classification
Xavier Mootoo, Alan A. Díaz-Montiel, Milad Lankarany, Hina Tabassum
Time Series Classification of Supraglacial Lakes Evolution over Greenland Ice Sheet
Emam Hossain, Md Osman Gani, Devon Dunmire, Aneesh Subramanian, Hammad Younas
LogoRA: Local-Global Representation Alignment for Robust Time Series Classification
Huanyu Zhang, Yi-Fan Zhang, Zhang Zhang, Qingsong Wen, Liang Wang
Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series
Donato Riccio, Fabrizio Maturo, Elvira Romano
Look Into the LITE in Deep Learning for Time Series Classification
Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble
Chang Dong, Zhengyang Li, Liangwei Zheng, Weitong Chen, Wei Emma Zhang