| Citekey | ZhuEtAl2016Matrix |
| Source Code | https://github.com/matrix-profile-foundation/tsmp |
| Learning type | unsupervised |
| Input dimensionality | univariate |
- System dependencies (
apt)- build-essential (make, gcc, ...)
- r-base
- R-packages
- tsmp
STOMP 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 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 stomp
def post_stomp(scores: np.ndarray, args: dict) -> np.ndarray:
window_size = args.get("hyper_params", {}).get("anomaly_window_size", 30)
if window_size < 4:
print("WARN: window_size must be at least 4. Dynamically fixing it by setting window_size to 4")
window_size = 4
return ReverseWindowing(window_size=window_size).fit_transform(scores)Computes the Matrix Profile and Profile Index for Univariate Time Series.
The Matrix Profile, has the potential to revolutionize time series data mining because of its generality, versatility, simplicity and scalability. In particular it has implications for time series motif discovery, time series joins, shapelet discovery (classification), density estimation, semantic segmentation, visualization, rule discovery, clustering etc.
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.
window_size(int): Size of the sliding window.exclusion_zone(numeric): Size of the exclusion zone, based on window size. See details. (Default is1/2).verbose(int): See details. (Default is1).n_jobs(int): Number of workers for parallel. (Default is1).
Returns a MatrixProfile object, a list with the matrix profile mp, profile index pi left and right matrix profile lmp, rmp and profile index lpi, rpi, window size w and exclusion zone ez.
# using threads
mp <- stomp_par(mp_toy_data$data[1:400, 1], window_size = 30, verbose = 0)Zhu Y, Zimmerman Z, Senobari NS, Yeh CM, Funning G. Matrix Profile II : Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins. Icdm. 2016 Jan 22;54(1):739-48.