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README.md

VALMOD
Citekey LinardiEtAl2020Matrix
Source Code https://github.com/matrix-profile-foundation/tsmp
Learning type unsupervised
Input dimensionality univariate

Original Dependencies

  • System dependencies (apt)
    • build-essential (make, gcc, ...)
    • r-base
  • R-packages
    • tsmp

Notes

VALMOD 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 output window size is equal to min_anomaly_window_size.

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 valmod
def post_valmod(scores: np.ndarray, args: dict) -> np.ndarray:
    window_min = args.get("hyper_params", {}).get("min_anomaly_window_size", 30)
    window_min = max(window_min, 4)
    return ReverseWindowing(window_size=window_min).fit_transform(scores)

Description: Variable Length Motif Discovery

Computes the Matrix Profile and Profile Index for a range of query window sizes.

Details

This algorithm uses an exact algorithm based on a novel lower bounding technique, which is specifically designed for the motif discovery problem. verbose changes how much information is printed by this function; 0 means nothing, 1 means text, 2 adds the progress bar, 3 adds the finish sound. exclusion_zone is used to avoid trivial matches; if a query data is provided (join similarity), this parameter is ignored.

Paper that implements skimp() suggests that window_max / window_min > 1.24 begins to weakening pruning in valmod().

Parameters

  • window_min (int): Minimum size of the sliding window.
  • window_max (int): Maximum size of the sliding window.
  • heap_size (int): Size of the distance profile heap buffer. (Default is 50).
  • exclusion_zone (numeric): Size of the exclusion zone, based on window size. See details. (Default is 1/2).
  • lb (logical): If FALSE all window sizes will be calculated using STOMP instead of pruning. This is just for academic purposes. (Default is TRUE). REMOVED!
  • verbose (int): See details. (Default is 1).

Returns a Valmod object, a list with the matrix profile mp, profile index pi left and right matrix profile lmp, rmp and profile index lpi, rpi, best window size w for each index and exclusion zone ez. Additionally: evolution_motif the best motif distance per window size, and non-length normalized versions of mp, pi and w: mpnn, pinn and wnn.

Example

mp <- valmod(mp_toy_data$data[1:200, 1], window_min = 30, window_max = 40, verbose = 0)
\donttest{
    ref_data <- mp_toy_data$data[, 1]
    query_data <- mp_toy_data$data[, 2]
    # self similarity
    mp <- valmod(ref_data, window_min = 30, window_max = 40)
    # join similarity
    mp <- valmod(ref_data, query_data, window_min = 30, window_max = 40)
}

Citation and Reference

Linardi M, Zhu Y, Palpanas T, Keogh E. VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series. In: Proceedings of the 2018 International Conference on Management of Data - SIGMOD '18. New York, New York, USA: ACM Press; 2018. p. 1757-60.

Website: http://www.cs.ucr.edu/~eamonn/MatrixProfile.html