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.
- 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
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)
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