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series2graph

README.md

Series2Graph (S2G)

Restricted Access!!
Citekey BoniolPalpanas2020Series2Graph
Source Code From Paul and Themis
Learning type unsupervised
Input dimensionality univariate

After receiving the original source code from the authors, place the directory series2graph into this folder.

Original Dependencies

  • python=3
  • networkx=2.2
  • numpy=1.15.4
  • scipy=1.1.0
  • pandas=0.23.4
  • matplotlib=3.0.2
  • scikit-learn=0.23.2

Notes

Series2Graph 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 + convolution_size) + query_window_size + 4.

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 s2g
def post_s2g(scores: np.ndarray, args: dict) -> np.ndarray:
    window_size = args.get("hyper_params", {}).get("window_size", 50)
    query_window_size = args.get("hyper_params", {}).get("query_window_size", 75)
    convolution_size = args.get("hyper_params", {}).get("convolution_size", window_size // 3)
    size = (window_size + convolution_size) + query_window_size + 4
    return ReverseWindowing(window_size=size).fit_transform(scores)

Copyright notice and citation format

Authors: Paul Boniol, Themis Palpanas, Mohammed Meftah, Emmanuel Remy Date: 08/07/2020 Copyright retained by the authors Algorithms protected by patent application FR2005261 Code provided as is, and can be used only for research purposes

Reference using:

P. Boniol and T. Palpanas, Series2Graph: Graph-based Subsequence Anomaly Detection in Time Series, PVLDB (2020)

P. Boniol and T. Palpanas and M. Meftah and E. Remy, GraphAn: Graph-based Subsequence Anomaly Detection, demo PVLDB (2020)