README.md

DeepAnT

Adapted version of the community implementation of DeepAnT from https://github.com/dev-aadarsh/DeepAnT.

Citekey	BasharNayak2020TAnoGAN
Source Code	https://github.com/dev-aadarsh/DeepAnT
Input Dimensionality	multivariate
Learning Type	semi-supervised

Dependencies

• python 3
• numpy
• pandas
• pytorch

Notes

DeepAnT outputs anomaly scores for windows. The results require post-processing. The scores for each point can be assigned by aggregating the anomaly scores for each window the point is included in. The window size is computed by window_size + prediction_window_size.

U can use the following code snippet for the post-processing step in TimeEval (default parameters directly filled in from the source code):

from timeeval.utils.window import ReverseWindowing
# post-processing for DeepAnT
def _post_deepant(scores: np.ndarray, args: dict) -> np.ndarray:
    window_size = args.get("hyper_params", {}).get("window_size", 45)
    prediction_window_size = args.get("hyper_params", {}).get("prediction_window_size", 1)
    size = window_size + prediction_window_size
    return ReverseWindowing(window_size=size).fit_transform(scores)