Framework for retrieve salient subsequences from a dataset

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)
)

Arguments

.mp: a TSMP object of class MatrixProfile.
data: the data used to build the Matrix Profile, if not embedded.
n_bits: an int or vector of int. Number of bits for MDL discretization. (Default is 8).
n_cand: an int. number of candidate when picking the subsequence in each iteration. (Default is 10).
exclusion_zone: if a number will be used instead of embedded value. (Default is NULL).
verbose: an int. See details. (Default is 2).

Value

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.

Details

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.

References

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/

Examples

# toy example
data <- mp_toy_data$data[, 1]
mp <- tsmp(data, window_size = 30, verbose = 0)
mps <- salient_subsequences(mp, data, verbose = 0)
# \donttest{
# full example
data <- mp_meat_data$sub$data
w <- mp_meat_data$sub$sub_len
mp <- tsmp(data, window_size = w, verbose = 2, n_workers = 2)
#> Warming up parallel with 2 cores.
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#> Error in {    work_len <- length(idx_work[[i]])    pro_muls <- matrix(Inf, matrix_profile_size, 1)    pro_idxs <- matrix(-Inf, matrix_profile_size, 1)    if (join) {        pro_muls_right <- pro_muls_left <- NULL        pro_idxs_right <- pro_idxs_left <- NULL    }    else {        pro_muls_right <- pro_muls_left <- pro_muls        pro_idxs_right <- pro_idxs_left <- pro_idxs    }    dist_pro <- matrix(0, matrix_profile_size, 1)    last_product <- matrix(0, matrix_profile_size, 1)    drop_value <- matrix(0, 1, 1)    for (j in 1:work_len) {        idx_st <- idx_work[[i]][1]        idx_ed <- idx_work[[i]][work_len]        idx <- idx_work[[i]][j]        query_window <- as.matrix(query[idx:(idx + window_size -             1), 1])        if (j == 1) {            nni <- dist_profile(data, query, nn, index = idx)            dist_pro[, 1] <- nni$distance_profile            last_product[, 1] <- nni$last_product        }        else {            last_product[2:(data_size - window_size + 1), 1] <- last_product[1:(data_size -                 window_size), 1] - data[1:(data_size - window_size),                 1] * drop_value + data[(window_size + 1):data_size,                 1] * query_window[window_size, 1]            last_product[1, 1] <- first_product[idx, 1]            dist_pro <- 2 * (window_size - (last_product - window_size *                 nni$par$data_mean * nni$par$query_mean[idx])/(nni$par$data_sd *                 nni$par$query_sd[idx]))        }        dist_pro[dist_pro < 0] <- 0        dist_pro <- sqrt(dist_pro)        drop_value <- query_window[1, 1]        if (exclusion_zone > 0) {            exc_st <- max(1, idx - exclusion_zone)            exc_ed <- min(matrix_profile_size, idx + exclusion_zone)            dist_pro[exc_st:exc_ed, 1] <- Inf        }        dist_pro[nni$par$data_sd < vars()$eps] <- Inf        if (skip_location[idx] || any(nni$par$query_sd[idx] <             vars()$eps)) {            dist_pro[] <- Inf        }        dist_pro[skip_location] <- Inf        if (!join) {            ind <- (dist_pro[idx:matrix_profile_size] < pro_muls_left[idx:matrix_profile_size])            ind <- c(rep(FALSE, (idx - 1)), ind)            pro_muls_left[ind] <- dist_pro[ind]            pro_idxs_left[which(ind)] <- idx            ind <- (dist_pro[1:idx] < pro_muls_right[1:idx])            ind <- c(ind, rep(FALSE, matrix_profile_size - idx))            pro_muls_right[ind] <- dist_pro[ind]            pro_idxs_right[which(ind)] <- idx        }        ind <- (dist_pro < pro_muls)        pro_muls[ind] <- dist_pro[ind]        pro_idxs[which(ind)] <- idx    }    res <- list(pro_muls = pro_muls, pro_idxs = pro_idxs, pro_muls_left = pro_muls_left,         pro_idxs_left = pro_idxs_left, pro_muls_right = pro_muls_right,         pro_idxs_right = pro_idxs_right)    res}: task 1 failed - "could not find function "mass_v3""
mps <- salient_subsequences(mp, data, n_bits = c(4, 6, 8), verbose = 2)
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#> Warning: number of rows of result is not a multiple of vector length (arg 2)
#> Finished in 0.05 secs
# }