论文标题
时间序列合成通过基于多尺度斑块的小波缩放图的生成
Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram
论文作者
论文摘要
根据低数据制度中的单个样本学习，提出了一个框架，用于无条件生成合成时间序列。该框架旨在使用单图生成模型捕获时间序列小波缩放图中的斑块分布，并生成合成时间序列的生成逼真的小波系数。证明该框架对于时间序列的忠诚度和多样性是有效的，没有趋势。同样，性能对于生成具有相同持续时间（改组）而不是更长的样本（重新定位）的样本更有希望。
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).
