Creating synthetic time series data

Description

Summary

• A step-by-step guide to creating high quality synthetic time-series datasets with Python TL;DR In this post, we will create synthetic versions of a time-series dataset using Gretel.ai’s synthetic data library, visualize and analyze the results, and discuss several use cases for synthetic time series data.
• The training dataset For our training dataset we will create a time series model at hourly intervals.
• Plotting the time series training set Extract trend data Next, we create the training set for our synthetic model.
• To match the time range of the original dataset, we’ll use Gretel’s seed_fields function, which allows you to pass in data to use as a prefix for each generated row.