  Data augmentation is a key regularization method to support the forecast and
classification performance of highly parameterized models in computer vision.
In the time series domain however, regularization in terms of augmentation is
not equally common even though these methods have proven to mitigate effects
from small sample size or non-stationarity. In this paper we apply state-of-the
art image-based generative models for the task of data augmentation and
introduce the extended intertemporal return plot (XIRP), a new image
representation for time series. Multiple tests are conducted to assess the
quality of the augmentation technique regarding its ability to synthesize time
series effectively and improve forecast results on a subset of the M4
competition. We further investigate the relationship between data set
characteristics and sampling results via Shapley values for feature attribution
on the performance metrics and the optimal ratio of augmented data. Over all
data sets, our approach proves to be effective in reducing the return forecast
error by 7% on 79% of the financial data sets with varying statistical
properties and frequencies.
