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.

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 using for computing. Defaults to `1`.
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)