Conditional GAN for timeseries generation

Abstract

It is abundantly clear that time dependent data is a vital source of information in the world. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an algorithm to perform well with limited data. This work attempts to ease the frustration by proposing a new architecture, Time Series GAN (TSGAN), to model realistic time series data. We evaluate TSGAN on 70 data sets from a benchmark time series database. Our results demonstrate that TSGAN performs better than the competition both quantitatively using the Frechet Inception Score (FID) metric, and qualitatively when classification is used as the evaluation criteria.

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Cited by in corpus (5)

• Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art
• Demand Forecasting of Individual Probability Density Functions with Machine Learning
• Statistical analysis of Wasserstein GANs with applications to time series forecasting
• Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks
• Feasibility of Modeling Orthogonal Frequency-Division Multiplexing Communication Signals with Unsupervised Generative Adversarial Networks