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DAuGAN: An Approach for Augmenting Time Series Imbalanced Datasets via Latent Space Sampling Using Adversarial Techniques - 2021

Daugan: An Approach For Augmenting Time Series Imbalanced Datasets Via Latent Space Sampling Using Adversarial Techniques

Research Area:  Machine Learning


Data augmentation is a commonly used technique in data science for improving the robustness and performance of machine learning models. The purpose of the paper is to study the feasibility of generating synthetic data points of temporal nature towards this end. A general approach named DAuGAN (Data Augmentation using Generative Adversarial Networks) is presented for identifying poorly represented sections of a time series, studying the synthesis and integration of new data points, and performance improvement on a benchmark machine learning model. The problem is studied and applied in the domain of algorithmic trading, whose constraints are presented and taken into consideration. The experimental results highlight an improvement in performance on a benchmark reinforcement learning agent trained on a dataset enhanced with DAuGAN to trade a financial instrument.


Author(s) Name:  Andrei Bratu and Gabriela Czibula

Journal name:  Scientific Programming

Conferrence name:  

Publisher name:  Hindawi

DOI:  10.1155/2021/7877590

Volume Information: