@inproceedings{
wen2024abstracted,
title={Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification},
author={Yunshi Wen and Tengfei Ma and Tsui-Wei Weng and Lam M. Nguyen and Anak Agung Julius},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=pwKkNSuuEs}
}
In time-series analysis, many recent works seek to provide a unified view and representation for time-series across multiple domains, leading to the development of foundation models for time-series data. Despite diverse modeling techniques, existing models are black boxes and fail to provide insights and explanations about their representations. In this paper, we present VQShape, a pre-trained, generalizable, and interpretable model for time-series representation learning and classification. By introducing a novel representation for time-series data, we forge a connection between the latent space of VQShape and shape-level features. Using vector quantization, we show that time-series from different domains can be described using a unified set of low-dimensional codes, where each code can be represented as an abstracted shape in the time domain. On classification tasks, we show that the representations of VQShape can be utilized to build interpretable classifiers, achieving comparable performance to specialist models. Additionally, in zero-shot learning, VQShape and its codebook can generalize to previously unseen datasets and domains that are not included in the pre-training process. The code and pre-trained weights are available at https://github.com/YunshiWen/VQShape.
Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification
Yunshi Wen Tengfei Ma Tsui-Wei Weng Lam M. Nguyen Anak Agung Julius