README.md

Subsequence adaption of Isolation Forest (IF)

Citekey	-
Source Code	-
Learning type	unsupervised
Input dimensionality	univariate

Notes

Subsequence IF 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.

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 sIF
def post_sIF(scores: np.ndarray, args: dict) -> np.ndarray:
    window_size = args.get("hyper_params", {}).get("window_size", 100)
    return ReverseWindowing(window_size=window_size).fit_transform(scores)