Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation

• URL: http://arxiv.org/abs/2408.16613v1
• Date: Thu, 29 Aug 2024 15:20:17 GMT
• Title: Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation
• Authors: Johan Vik Mathisen, Erlend Lokna, Daesoo Lee, Erlend Aune,
• Abstract summary: We introduce a novel framework, termed NC-VQVAE, to integrate self-supervised learning into time series generation approaches. Our experimental results demonstrate that NC-VQVAE results in a considerable improvement in the quality of synthetic samples.
• Score: 0.8999666725996975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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