Forecasting with Synthetic Data at Scale (Nixtla & YData)

In this post, we explain how to use nixtlats and ydata-synthetic, open-source and free python libraries that allow you to generate synthetic data to train state-of-the-art deep learning models without any significant loss of data quality. We develop a deep learning forecasting pipeline without direct access to the original data and show that synthetic data has a minimal impact on the performance of the models. In the last decade, neural network-based forecasting methods have become ubiquitous in large-scale forecasting applications, transcending industry boundaries into academia, as it has redefined the state-of-the-art in many practical tasks like demand planning, electricity load forecasting, reverse logistics, weather forecasting, as well as forecasting competitions like the M4 and M5. However, one of the problems for those interested in creating forecasts is model development or software testing without using original data; this may be because the actual data takes time to collect, there are restrictions on its use, or the data simply does not exist. In many applications, the user does not want the model to have access to the actual data, in particular, if the model training is done in the cloud or outside one's infrastructure.