Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913485546323968 |
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| author | Mathisen, Johan Vik Lokna, Erlend Lee, Daesoo Aune, Erlend |
| author_facet | Mathisen, Johan Vik Lokna, Erlend Lee, Daesoo Aune, Erlend |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16613 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation Mathisen, Johan Vik Lokna, Erlend Lee, Daesoo Aune, Erlend Machine Learning 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. |
| title | Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.16613 |