salient_subsequences | R Documentation |
In order to allow a meaningful visualization in Multi-Dimensional Space (MDS), this function retrieves the most relevant subsequences using Minimal Description Length (MDL) framework.
salient_subsequences( .mp, data, n_bits = 8, n_cand = 10, exclusion_zone = NULL, verbose = getOption("tsmp.verbose", 2) )
.mp |
a TSMP object of class |
data |
the data used to build the Matrix Profile, if not embedded. |
n_bits |
an |
n_cand |
an |
exclusion_zone |
if a |
verbose |
an |
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.
Returns the input .mp
object with a new name salient
. It contains: indexes
, a vector
with the starting position of each subsequence, idx_bit_size
, a vector
with the associated
bitsize for each iteration and bits
the value used as input on n_bits
.
Yeh CCM, Van Herle H, Keogh E. Matrix profile III: The matrix profile allows visualization of salient subsequences in massive time series. Proc - IEEE Int Conf Data Mining, ICDM. 2017;579-88.
Hu B, Rakthanmanon T, Hao Y, Evans S, Lonardi S, Keogh E. Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL. In: 2011 IEEE 11th International Conference on Data Mining. IEEE; 2011. p. 1086-91.
Website: https://sites.google.com/site/salientsubs/
# toy example data <- mp_toy_data$data[, 1] mp <- tsmp(data, window_size = 30, verbose = 0) mps <- salient_subsequences(mp, data, verbose = 0) # full example data <- mp_meat_data$sub$data w <- mp_meat_data$sub$sub_len mp <- tsmp(data, window_size = w, verbose = 2, n_workers = 2) mps <- salient_subsequences(mp, data, n_bits = c(4, 6, 8), verbose = 2)
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