diss.CID | R Documentation |
Computes the distance based on the Euclidean distance corrected by the complexity estimation of the series.
diss.CID(x, y)
x |
Numeric vector containing the first of the two time series. |
y |
Numeric vector containing the second of the two time series. |
This distance is defined
CID(x,y) = ED(x,y) \times CF(x,y)
where CF(x,y)
is a complexity correction factor defined as:
max(CE(x), CE(y)) / min(CE(x), CE(y))
and CE(x)
is a compexity estimate of a time series x
. diss.CID
therefore increases the distance between series with different complexities. If the series have the same complexity estimate, the distance defenerates Euclidean distance. The complexity is defined in diss.CID
as:
CE(x) = \sqrt{ \sum_{t=1} (x_{t+1} - x_t)^2 }
The computed dissimilarity.
Pablo Montero Manso, José Antonio Vilar.
Batista, G. E., Wang, X., & Keogh, E. J. (2011). A Complexity-Invariant Distance Measure for Time Series. In SDM (Vol. 31, p. 32).
Montero, P and Vilar, J.A. (2014) TSclust: An R Package for Time Series Clustering. Journal of Statistical Software, 62(1), 1-43. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.18637/jss.v062.i01")}
diss
, diss.CORT
n = 100
x <- rnorm(n) #generate sample series, white noise and a wiener process
y <- cumsum(rnorm(n))
diss.CID(x, y)
z <- rnorm(n)
w <- cumsum(rnorm(n))
series = rbind(x, y, z, w)
diss(series, "CID")
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