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Mantis: Lightweight Foundation Model for Time Series Classification
Vasilii Feofanov, Songkang Wen, Shifeng Xie +10
While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly f…
User-friendly Foundation Model Adapters for Multivariate Time Series Classification
Vasilii Feofanov, Romain Ilbert, Malik Tiomoko +2
Foundation models, while highly effective, are often resource-intensive, requiring substantial inference time and memory. This paper addresses the challenge of making these models…
Data Augmentation for Multivariate Time Series Classification: An Experimental Study
Romain Ilbert, Thai V. Hoang, Zonghua Zhang
Our study investigates the impact of data augmentation on the performance of multivariate time series models, focusing on datasets from the UCR archive. Despite the limited size of…
SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov +4
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multiv…
Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting
Romain Ilbert, Thai V. Hoang, Zonghua Zhang +1
Balancing the trade-off between accuracy and robustness is a long-standing challenge in time series forecasting. While most of existing robust algorithms have achieved certain subo…