Kavli Affiliate: Cheng Peng
| First 5 Authors: Yuxuan Chen, Shanshan Huang, Yunyao Cheng, Peng Chen, Zhongwen Rao
| Summary:
Time series classification (TSC) is an important task in time series
analysis. Existing TSC methods mainly train on each single domain separately,
suffering from a degradation in accuracy when the samples for training are
insufficient in certain domains. The pre-training and fine-tuning paradigm
provides a promising direction for solving this problem. However, time series
from different domains are substantially divergent, which challenges the
effective pre-training on multi-source data and the generalization ability of
pre-trained models. To handle this issue, we introduce Augmented Series and
Image Contrastive Learning for Time Series Classification (AimTS), a
pre-training framework that learns generalizable representations from
multi-source time series data. We propose a two-level prototype-based
contrastive learning method to effectively utilize various augmentations in
multi-source pre-training, which learns representations for TSC that can be
generalized to different domains. In addition, considering augmentations within
the single time series modality are insufficient to fully address
classification problems with distribution shift, we introduce the image
modality to supplement structural information and establish a series-image
contrastive learning to improve the generalization of the learned
representations for TSC tasks. Extensive experiments show that after
multi-source pre-training, AimTS achieves good generalization performance,
enabling efficient learning and even few-shot learning on various downstream
TSC datasets.
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