Time series synthesis using GANs - A take on DoppelGANger

Abstract

With a growing need for data comes a growing need for synthetic data. In this work we reproduce the results of DoppelGANger [16] in synthesising time series data with metadata. We identify a key issue in the comparison made in [16] of DoppelGANger to TimeGAN, RNNs, AR and HMM models, which creates a new avenue of time series synthesis using GANs. We show that not all results of [16] can be reproduced. We furthermore find that DoppelGANger does not adequately capture measurement-metadata correlations of our dataset. Sample size reduction is shown to be an effective tool to reduce training time while still attaining accurate results, and the key parameter S is tuned further. Finally we show that execution on CPU has similar training times as execution on GPU by [16], suggesting that the original code can be improved, and we release our version of the models ourselves, to enable easy reproduction. In closing points we shine light on possible future improvements that we were unable to test ourselves, and conclude that DoppelGANger is a promising model that opens the door to new unseen applications of GANs for time series synthesis.