Additional augmentations

[jankrepl]

Rather than reinventing the wheel one could just use torchvision transforms https://pytorch.org/docs/stable/torchvision/transforms.html

• Compose (already recreated in deepdow)
• RandomApply - apply all with some probability
• RandomChoice - apply exactly one but at random
• RandomOrder - apply all but in random order
• 1Dwarping - Affine would be a special case, one could in theory have any increasing function (derivative > 0)
• RandomAffine - scaling and translation along the y axis (lookback) could be a brilliant augmentation for deepdow tensors
• RandomHorizontalFlip - flipping the time flow, probably super confusing if one wants to pic up mean reversion
• Normalize - a must together with some helper function that computes means, stds in the training set. However, it still assumes that the time series is stationary.
• RandomErasing - (similar to the current Dropout however it is contiguous regions)

Additionally, torchvision might be also helpful in other tasks (see #39)

The clear downside is introducing yet another dependency. Additionally, one might argue that it is better to go all the way and use imgaug, albumentation,...

Other nonvision augmentations:

• https://arxiv.org/abs/2002.12478
• https://github.com/arundo/tsaug
• https://halshs.archives-ouvertes.fr/halshs-01357973/document