ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation

TL;DR

This framework benefits from a time series-based loss function and oversight from a supervisory net-work, both of which capture the stepwise conditional distributions of the data effectively and introduces an early generation algorithm and an improved neural network architecture to enhance stability and ensure effective generalization across both short and long time series.

Abstract

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.

Figures (6)

• Figure 1: The figure illustrates the architecture of ChronoGAN for time series generation. ChronoGAN consists of five neural networks, each utilizing sequence-to-sequence GRU-LSTM layers. These networks are trained jointly to learn the probability distribution of real data and to capture the temporal dynamics inherent in the real samples.
• Figure 2: GRU-LSTM Network Architecture: The figure illustrates the architecture of a GRU-LSTM model for univariate time series data, featuring multiple layers of LSTM and GRU cells (in this case, two layers) trained separately. These layers are then combined through perceptron or fully connected neural network layers. For multivariate time series data, multiple instances of these components are trained in parallel.
• Figure 3: This figure illustrates the original Sines dataset samples (top) and their corresponding synthetic counterparts generated by the ChronoGAN algorithm (bottom). Each subplot shows one of four randomly selected samples.
• Figure 4: Displayed here are original ECG dataset samples (top) and the synthetic data generated by ChronoGAN (bottom).
• Figure 5: PCA visualizations illustrate the distributional alignment between original and synthetic data samples generated by ChronoGAN and other baselines across our four datasets.