mass_v2: Calculates the distance profile using MASS_V2 algorithm
In franzbischoff/tsmp: Time Series with Matrix Profile

Description Usage Arguments Value References See Also Examples

Mueen's Algorithm for Similarity Search is The Fastest Similarity Search Algorithm for Time Series Subsequences under Euclidean Distance and Correlation Coefficient.

mass_v2(
  query_window,
  window_size,
  data_fft,
  data_size,
  data_mean,
  data_sd,
  query_mean,
  query_sd,
  ...
)

`query_window`	a `vector` of `numeric`. Query window.
`window_size`	an `int`. Sliding window size.
`data_fft`	precomputed data product.
`data_size`	an `int`. The length of the reference data.
`data_mean`	precomputed data moving average.
`data_sd`	precomputed data moving standard deviation.
`query_mean`	precomputed query average.
`query_sd`	precomputed query standard deviation.
`...`	just a placeholder to catch unused parameters.

Returns the distance_profile for the given query and the last_product for STOMP algorithm.

Abdullah Mueen, Yan Zhu, Michael Yeh, Kaveh Kamgar, Krishnamurthy Viswanathan, Chetan Kumar Gupta and Eamonn Keogh (2015), The Fastest Similarity Search Algorithm for Time Series Subsequences under Euclidean Distance

Website: https://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html

mass_pre() to precomputation of input values.

w <- mp_toy_data$sub_len
ref_data <- mp_toy_data$data[, 1]
query_data <- mp_toy_data$data[, 1]
d_size <- length(ref_data)
q_size <- length(query_data)

pre <- tsmp:::mass_pre(ref_data, query_data, w)

dp <- list()
for (i in 1:(d_size - w + 1)) {
  dp[[i]] <- tsmp:::mass_v2(
    query_data[i:(i - 1 + w)], pre$window_size,
    pre$data_fft, pre$data_size, pre$data_mean, pre$data_sd,
    pre$query_mean[i], pre$query_sd[i]
  )
}