norma

NormA

Restricted Access!!

Citekey	BoniolEtAl2021Unsupervised
Source Code	From Paul and Themis
Learning type	unsupervised
Input dimensionality	univariate

After receiving the original source code from the authors, place the directories C and normats into this folder.

Original Dependencies

• python==3.6
• numpy==1.15.4
• pandas==0.23.4
• scipy==1.1.0
• tqdm==4.28.1
• tslearn==0.1.29 (requires cython)
• bundled dependency:
  • matrix_profile==0.1

Notes

NormA 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 2 * (anomaly_window_size - 1) + 1.

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 norma
def _post_norma(scores: np.ndarray, args: dict) -> np.ndarray:
    window_size = args.get("hyper_params", {}).get("anomaly_window_size", 20)
    size = 2 * window_size - 1
    return ReverseWindowing(window_size=size).fit_transform(scores)

Copyright notice

Authors: Paul Boniol, Michele Linardi, Federico Roncallo, Themis Palpanas Date: 08/07/2020 copyright retained by the authors algorithms protected by patent application FR2003946 code provided as is, and can be used only for research purposes