Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram
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Abstract (arXiv)
A framework is proposed for the unconditional generation of synthetic time series based on learning from a single sample in low-data regime case. The framework aims at capturing the distribution of patches in wavelet scalogram of time series using single image generative models and producing realistic wavelet coefficients for the generation of synthetic time series. It is demonstrated that the framework is effective with respect to fidelity and diversity for time series with insignificant to no trends. Also, the performance is more promising for generating samples with the same duration (reshuffling) rather than longer ones (retargeting).
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 21 Oct 2022.
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11 of 18 references.
References · 11
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