Synthetic Data Generation#
Chronos provides simulators to generate synthetic time series data for users who want to conquer limited data access in a deep learning/machine learning project or only want to generate some synthetic data to play with.
DPGANSimulator is the only simulator chronos provides at the moment, more simulators are on their way.
DPGANSimulator adopt DoppelGANger raised in Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions. The method is data-driven unsupervised method based on deep learning model with GAN (Generative Adversarial Networks) structure. The model features a pair of seperate attribute generator and feature generator and their corresponding discriminators
DPGANSimulator also supports a rich and comprehensive input data (training data) format and outperform other algorithms in many evalution metrics.
We reimplement this model by pytorch(original implementation was based on tf1) for better performance(both speed and memory).
Users may refer to detailed API doc.