Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation
<|reference_start|>Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation: State-of-the-art approaches in time series generation (TSG), such as TimeVQVAE, utilize vector quantization-based tokenization to effectively model complex distributions of time series. These approaches first learn to transform time series into a sequence of discrete latent vectors, and then a prior model is learned to model the sequence. The discrete latent vectors, however, only capture low-level semantics (\textit{e.g.,} shapes). We hypothesize that higher-fidelity time series can be generated by training a prior model on more informative discrete latent vectors that contain both low and high-level semantics (\textit{e.g.,} characteristic dynamics). In this paper, we introduce a novel framework, termed NC-VQVAE, to integrate self-supervised learning into those TSG methods to derive a discrete latent space where low and high-level semantics are captured. Our experimental results demonstrate that NC-VQVAE results in a considerable improvement in the quality of synthetic samples.<|reference_end|>
@article{mathisen2024blending,
    title={Blending Low and High-Level Semantics of Time Series for Better Masked
  Time Series Generation},
    author={Johan Vik Mathisen, Erlend Lokna, Daesoo Lee and Erlend Aune},
    journal={arXiv preprint arXiv:2408.16613},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16613},
    primaryClass={cs.LG}
}