ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation
This work addresses time series generation problems for researchers and practitioners, though it appears incremental as it builds on existing GAN and Autoencoder methods.
The paper tackled challenges in generating time series data with GANs, such as slow convergence and instability, by introducing a framework that integrates Autoencoder embeddings with adversarial training, resulting in consistent outperformance of existing benchmarks across diverse datasets.
Generating time series data using Generative Adversarial Networks (GANs) presents several prevalent challenges, such as slow convergence, information loss in embedding spaces, instability, and performance variability depending on the series length. To tackle these obstacles, we introduce a robust framework aimed at addressing and mitigating these issues effectively. This advanced framework integrates the benefits of an Autoencoder-generated embedding space with the adversarial training dynamics of GANs. This framework benefits from a time series-based loss function and oversight from a supervisory network, both of which capture the stepwise conditional distributions of the data effectively. The generator functions within the latent space, while the discriminator offers essential feedback based on the feature space. Moreover, we introduce an early generation algorithm and an improved neural network architecture to enhance stability and ensure effective generalization across both short and long time series. Through joint training, our framework consistently outperforms existing benchmarks, generating high-quality time series data across a range of real and synthetic datasets with diverse characteristics.