mass_v3 {tsmp} | R Documentation |
Calculates the distance profile using MASS_V3 algorithm
Description
Mueen's Algorithm for Similarity Search is The Fastest Similarity Search Algorithm for Time Series Subsequences under Euclidean Distance and Correlation Coefficient.
Usage
mass_v3(
query_window,
data,
window_size,
data_size,
data_mean,
data_sd,
query_mean,
query_sd,
k = NULL,
...
)
Arguments
query_window |
a |
data |
a |
window_size |
an |
data_size |
an |
data_mean |
precomputed data moving average. |
data_sd |
precomputed data moving standard deviation. |
query_mean |
precomputed query average. |
query_sd |
precomputed query standard deviation. |
k |
an |
... |
just a placeholder to catch unused parameters. |
Details
This is a piecewise version of MASS that performs better when the size of the pieces are well aligned with the hardware.
Value
Returns the distance_profile
for the given query and the last_product
for STOMP
algorithm.
References
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
See Also
mass_pre()
to precomputation of input values.
Examples
w <- mp_toy_data$sub_len
ref_data <- mp_toy_data$data[, 1]
query_data <- mp_toy_data$data[, 1]
d_size <- length(ref_data)
q_size <- length(query_data)
pre <- tsmp:::mass_pre(ref_data, query_data, w)
dp <- list()
for (i in 1:(d_size - w + 1)) {
dp[[i]] <- tsmp:::mass_v3(
query_data[i:(i - 1 + w)], ref_data,
pre$window_size, pre$data_size, pre$data_mean, pre$data_sd,
pre$query_mean[i], pre$query_sd[i]
)
}