FFAD: A Novel Metric for Assessing Generated Time Series Data Utilizing
  Fourier Transform and Auto-encoder
Yang Chen Dustin J. Kempton Rafal A. Angryk
The success of deep learning-based generative models in producing realistic
images, videos, and audios has led to a crucial consideration: how to
effectively assess the quality of synthetic samples. While the Fr\'{e}chet
Inception Distance (FID) serves as the standard metric for evaluating
generative models in image synthesis, a comparable metric for time series data
is notably absent. This gap in assessment capabilities stems from the absence
of a widely accepted feature vector extractor pre-trained on benchmark time
series datasets. In addressing these challenges related to assessing the
quality of time series, particularly in the context of Fr\'echet Distance, this
work proposes a novel solution leveraging the Fourier transform and
Auto-encoder, termed the Fr\'{e}chet Fourier-transform Auto-encoder Distance
(FFAD). Through our experimental results, we showcase the potential of FFAD for
effectively distinguishing samples from different classes. This novel metric
emerges as a fundamental tool for the evaluation of generative time series
data, contributing to the ongoing efforts of enhancing assessment methodologies
in the realm of deep learning-based generative models.