![]() ![]() As a GAN, the model uses an adversarial training scheme to simultaneously optimize the discriminator (or critic) and generator networks by comparing synthetic and real data. In this article, we give a brief overview of the DoppelGANger model, provide sample usage of our PyTorch implementation, and demonstrate excellent synthetic data quality on a task synthesizing daily wikipedia web traffic with a ~40x runtime speedup compared to the TensorFlow 1 implementation.ĭoppelGANger is based on a generative adversarial network ( GAN) with some modifications to better fit the time series generation task. As part of that work, we reimplemented the DoppelGANger model in PyTorch and are thrilled to release it as part of our open source gretel-synthetics library. al.) and are in the process of incorporating this model into our APIs and console. We really liked the DoppelGANger model and associated paper ( Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions by Lin et. The additional dimension of time where trends and correlations across time are just as important as correlations between variables creates added challenges for synthetic data.Īt Gretel, we’ve previously published blogs on synthesizing time series data ( financial data, time series basics), but are always looking at new models that can improve our synthetic data generation. Some applications for synthetic time series data include sensor readings, timestamped log messages, financial market prices, and medical records. Just as with tabular data, we often want to generate synthetic time series data to protect sensitive information or create more training data when real data is rare. Time series data, a sequence of measurements of the same variables across multiple points in time, is ubiquitous in the modern data world. ![]()
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