damp

DAMP

Source Code	own
Learning type	unsupervised
Input dimensionality	multivariate

This approach uses the DAMP algorithm to find anomalies. Its functionality is described in this paper.

Output Format

The output will be an anomaly score for every subsequence of size anomaly_window_size.

Dependencies

• python 3
• numpy
• pandas
• stumpy

Notes

from timeeval.utils.window import ReverseWindowing
# post-processing for damp
def post_damp(scores: np.ndarray, args: dict) -> np.ndarray:
    window_size = args.get("hyper_params", {}).get("anomaly_window_size", 50)
    return ReverseWindowing(window_size=window_size).fit_transform(scores)