  Data augmentation methods have been shown to be a fundamental technique to
improve generalization in tasks such as image, text and audio classification.
Recently, automated augmentation methods have led to further improvements on
image classification and object detection leading to state-of-the-art
performances. Nevertheless, little work has been done on time-series data, an
area that could greatly benefit from automated data augmentation given the
usually limited size of the datasets. We present two sample-adaptive automatic
weighting schemes for data augmentation: the first learns to weight the
contribution of the augmented samples to the loss, and the second method
selects a subset of transformations based on the ranking of the predicted
training loss. We validate our proposed methods on a large, noisy financial
dataset and on time-series datasets from the UCR archive. On the financial
dataset, we show that the methods in combination with a trading strategy lead
to improvements in annualized returns of over 50$\%$, and on the time-series
data we outperform state-of-the-art models on over half of the datasets, and
achieve similar performance in accuracy on the others.
