Computes the Distance between the 'data' and the 'query'.

This algorithm will use a rolling window, to computes the distance thorough the whole data. This means that the minimum distance found is the motif and the maximum distance is the discord on that time series. Attention you need first to create an object using mass_pre(). Read below.

Usage

mass(
  pre_obj,
  data,
  query = data,
  index = 1,
  version = c("v3", "v2"),
  n_workers = 1
)

mass_pre(
  data,
  window_size,
  query = NULL,
  type = c("normalized", "non_normalized", "absolute", "weighted"),
  weights = NULL
)

Arguments

pre_obj: Required. This is the object resulting from mass_pre(). The is no MASS without a pre.
data: Required. Any 1-dimension series of numbers (matrix, vector, ts etc.)
query: Optional. Accepts the same types as data and is used for join-similarity. Defaults to data for self-similarity. IMPORTANT Use the same data used on mass_pre().
index: An integer. This is the index of the rolling window that will be used. Must be between 1 and length(data) - window_size + 1.
version: A string. Chooses the version of MASS what will be used. Ignored if mass_pre() is not the "normalized" type.
n_workers: An integer. The number of threads used for computing. Defaults to 1. For parallel execution across processes, prefer a PSOCK cluster and use n_workers = 1 within each worker. See matrixprofiler for fork limitations.
window_size: Required. An integer defining the rolling window size.
type: This changes how the MASS algorithm will compare the rolling window and the data. (See details).
weights: Optional. It is used when the type is weighted, and has to be the same size as the window_size.

Value

mass() returns a list with the distance_profile and the last_product that is only useful for computing the Matrix Profile.

mass_pre() returns a list with several precomputations to be used on MASS later. Attention use this before mass().

Details

There are currently four ways to compare the window with the data:

normalized: this normalizes the data and the query window. This is the most frequently used.
non_normalized: this won't normalize the query window. The data still being normalized.
absolute: this won't normalize both the data and the query window.
weighted: this normalizes the data and query window, and also apply a weight vector on the query.

Examples

pre <- mass_pre(motifs_discords_small, 50)
dist_profile <- mass(pre, motifs_discords_small)
pre <- mass_pre(motifs_discords_small, 50)
dist_profile <- mass(pre, motifs_discords_small)