Synthetic Time Series

Synthetic time series generation focuses on creating artificial datasets that mimic real-world temporal patterns, primarily to address data scarcity, enhance model training, and improve the interpretability of time series analysis. Current research emphasizes the development and evaluation of generative models, including GANs, diffusion models, and transformer-based architectures, often incorporating techniques like wavelet transforms and conditional generation to control the characteristics of the synthetic data. This field is crucial for advancing machine learning applications in various domains, from anomaly detection in cloud computing and forecasting in finance to improving the accuracy and efficiency of time series classification in neuroscience and other data-limited areas.