This article provides a step-by-step guide to creating high quality synthetic time-series datasets using Python. It discusses the use of synthetic data for generating large amounts of training data, especially in cases where there is limited real-world data available. The author explains how Gretel.ai's synthetic data library can be used to create synthetic versions of a time-series dataset and visualize and analyze the results. They also discuss several use cases for synthetic time series data. The article provides code examples and demonstrates how to generate synthetic datasets, train a model on them, and compare the results with the original training set.