  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).
