IncDTW (version 1.1.4.4)

idtw: Incremental DTW

Description

Update the DTW distance, cost matrices and direction matrices including the warping path for new observations of two time series.

Usage

idtw(Q, C, newObs, gcm, dm, 
      dist_method = c("norm1", "norm2", "norm2_square"),
      step_pattern = c("symmetric2", "symmetric1"),
      diffM = NULL, ws = NULL, 
      return_cm = FALSE,
      return_diffM = FALSE,
      return_wp = FALSE,
      return_diffp = FALSE,
      return_QC = FALSE)

Arguments

Q

numeric vector, or matrix (see also dtw)

C

numeric vector, or matrix

newObs

vector or matrix of new observations to be appended to C

gcm

global cost matrix, output from dtw(Q, C, ...)

dm

direction matrix, output from dtw(Q, C, ...)

dist_method

character, describes the method of distance measure. See also dtw.

step_pattern

character, describes the step pattern. See also dtw.

diffM

differences matrix, output from dtw(Q, C, ...). This matrix is an optional input parameter (default = NULL) that is necessary to return the path of differences. Only for univariate time series Q and C.

ws

integer, describes the window size for the sakoe chiba window. If NULL, then no window is applied. (default = NULL)

return_cm

logical, if TRUE then the Matrix of costs (the absolute value) is returned. (default = FALSE)

return_diffM

logical, if TRUE then the Matrix of differences (not the absolute value) is returned. (default = FALSE)

return_wp

logical, if TRUE then the warping path is returned. (default = FALSE) If return_diffp == TRUE, then return_wp is set to TRUE as well.

return_diffp

logical, if TRUE then the path of differences (not the absolute value) is returned. (default = FALSE)

return_QC

logical, if TRUE then the input vectors Q and C are appended to the returned list. This is useful for the plot.idtw function. (default = FALSE)

Value

distance

the DTW distance, that is the element of the last row and last column of gcm

gcm

global cost matrix

dm

direction matrix (3=up, 1=diagonal, 2=left)

wp

warping path

ii

indices of Q of the optimal path

jj

indices of C of the optimal path

cm

Matrix of costs

diffM

Matrix of differences

diffp

path of differences

Q

input Q

C

input C

normalized_distance

the normalized DTW distance, see also link{dtw}

Details

The dynamic time warping distance is the element in the last row and last column of the global cost matrix.

References

• Leodolter, M.; Pland, C.; Br<U+00E4>ndle, N; IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping. Journal of Statistical Software, 99(9), 1-23. 10.18637/jss.v099.i09

• Sakoe, H.; Chiba, S., Dynamic programming algorithm optimization for spoken word recognition, Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on , vol.26, no.1, pp. 43-49, Feb 1978. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1163055

Examples

# NOT RUN {
#--- Compare the incremental calculation with the basic
# calculation from scratch.
Q <- cumsum(rnorm(100))
C <- Q[11:100] + rnorm(90, 0, 0.5)
newObs <-  c(2, 3)# new observation
base <- dtw(Q = Q, C = C, ws = 15, return_diffM = TRUE) 
base

# recalculation from scratch with new observations
result0 <- dtw(Q = Q, C = c(C, newObs), ws = 15,  return_diffM = TRUE)

# the incremental step with new observations
result1 <- idtw(Q, C, ws = 15, newO = newObs, gcm = base$gcm, 
                dm = base$dm, diffM = base$diffM, return_diffp = TRUE,  
                return_diffM = TRUE, return_QC = TRUE) 
print(result1, digits = 2)
plot(result1)


#--- Compare the incremental calculation with external calculated 
# costMatrix cm_add with the basic calculation from scratch.
cm_add <- cm(Q, newObs)
result2 <- idtw(Q = cm_add, C = "cm_add", ws = 15, newO = newObs, 
                gcm = base$gcm, dm = base$dm) 

c(result0$distance, result1$distance, result2$distance)


# }