Impact of Conditioning Strategies on Fidelity of Synthetic Financial Time-Series Generated by TimeGANs and VAEs

Abstract

Abstract: Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data

Research Question

What is the impact of different conditioning strategies on the fidelity of synthetic financial time-series generated by TimeGANs versus VAEs?

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