2022/10/21 by Kazemi, Amir, Hadi Meidani, Meidani, Hadi · 1 citation
Computer Science · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Complex Systems and Time Series Analysis #Data Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Machine Learning (cs.LG) #Neural Networks and Applications #Signal Processing (eess.SP) #Statistics and Probability (physics.data-an) #Time Series Analysis and Forecasting #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.02620
openalex publication_date 2022/10/21 · openalex created_date 2022/11/12 · openalex updated_date 2026/07/28
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).