Function mstump

stumpy/mstump.py:1114–1287

Compute the multi-dimensional z-normalized matrix profile This is a convenience wrapper around the Numba JIT-compiled parallelized ``_mstump`` function which computes the multi-dimensional matrix profile and multi-dimensional matrix profile index according to mSTOMP, a variant of

(
    T, m, include=None, discords=False, normalize=True, p=2.0, T_subseq_isconstant=None
)

@core.non_normalized(maamp, exclude=["normalize", "T_subseq_isconstant"])
def mstump(
    T, m, include=None, discords=False, normalize=True, p=2.0, T_subseq_isconstant=None
):
    """
    Compute the multi-dimensional z-normalized matrix profile

    This is a convenience wrapper around the Numba JIT-compiled parallelized
    ``_mstump`` function which computes the multi-dimensional matrix profile and
    multi-dimensional matrix profile index according to mSTOMP, a variant of
    mSTAMP. Note that only self-joins are supported.

    Parameters
    ----------
    T : numpy.ndarray
        The time series or sequence for which to compute the multi-dimensional
        matrix profile. Each row in ``T`` represents data from the same
        dimension while each column in ``T`` represents data from a different
        dimension.

    m : int
        Window size.

    include : list, numpy.ndarray, default None
        A list of (zero-based) indices corresponding to the dimensions in ``T`` that
        must be included in the constrained multidimensional motif search.
        For more information, see Section IV D in:

        `DOI: 10.1109/ICDM.2017.66 \
        <https://www.cs.ucr.edu/~eamonn/Motif_Discovery_ICDM.pdf>`__

    discords : bool, default False
        When set to ``True``, this reverses the distance matrix which results in a
        multi-dimensional matrix profile that favors larger matrix profile values
        (i.e., discords) rather than smaller values (i.e., motifs). Note that indices
        in ``include`` are still maintained and respected.

    normalize : bool, default True
        When set to ``True``, this z-normalizes subsequences prior to computing
        distances. Otherwise, this function gets re-routed to its complementary
        non-normalized equivalent set in the ``@core.non_normalized`` function
        decorator.

    p : float, default 2.0
        The p-norm to apply for computing the Minkowski distance. Minkowski distance is
        typically used with ``p`` being ``1`` or ``2``, which correspond to the
        Manhattan distance and the Euclidean distance, respectively. This parameter is
        ignored when ``normalize == True``.

    T_subseq_isconstant : numpy.ndarray, function, or list, default None
        A parameter that is used to show whether a subsequence of a time series in ``T``
        is constant (``True``) or not. ``T_subseq_isconstant`` can be a 2D boolean
        ``numpy.ndarray`` or a function that can be applied to each time series in
        ``T``. Alternatively, for maximum flexibility, a list (with length equal to the
        total number of time series) may also be used. In this case,
        ``T_subseq_isconstant[i]`` corresponds to the ``i``-th time series ``T[i]``
        and each element in the list can either be a 1D boolean ``numpy.ndarray``, a
        function, or ``None``.