Description Usage Arguments Details Value References See Also Examples
Dividing time series into interval sequences of qualitative
features and determining the similarity of the qualitative behavior by
means of the length of LCS
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  qvalLCS(o, p,
o.t = seq(0, 1, length.out = length(o)),
p.t = seq(0, 1, length.out = length(p)),
smooth = c("none", "both", "obs", "sim"),
feature = c("f.slope", "f.curve", "f.steep", "f.level"))
## S3 method for class 'qvalLCS'
print(x, ...)
## S3 method for class 'qvalLCS'
plot(x, y = NULL, ..., xlim = range(c(x$obs$x, x$sim$x)),
ylim = range(c(x$obs$y, x$sim$y)), xlab = "time", ylab = " ",
col.obs = "black", col.pred = "red",
plot.title = paste("LLCS =", x$lcs$LLCS, ", QSI =", x$lcs$QSI),
legend = TRUE)
## S3 method for class 'qvalLCS'
summary(object, ...)

o 
vector of observed values 
p 
vector of predicted values 
o.t 
vector of observation times 
p.t 
vector of times for predicted values 
smooth 
character string to decide if values should be smoothed
before validation, default no smoothing 
feature 
one of 
x 
a result from a call of 
y 
y unused 
... 
further parameters to be past to

xlim 
the size of the plot in xdirection 
ylim 
the size of the plot in ydirection 
xlab 
the label of the xaxis of the plot 
ylab 
the label of the yaxis of the plot 
col.obs 
color to plot the observations 
col.pred 
color to plot the predictions 
plot.title 
title for the plot 
legend 
tegend for the plot 
object 
a result from a call of 
Common quantitative deviance measures underestimate the
similarity of patterns if there are shifts in time between measurement
and simulation. These methods also assume compareable values in each
time series of the whole time sequence. To compare values independent
of time the qualitative behavior of the time series could be
analyzed. Here the time series are divided into interval sequences
of their local shape. The comparison occurs on the basis of these
segments and not with the original time series. Here shifts in time
are possible, i.e. missing or additional segments are acceptable
without losing similarity. The dynamic programming algorithm of
the longest common subsequence LCS
is used to determine
QSI
as index of similarity of the patterns.
If selected the data are smoothed using a weighted average and a
Gaussian curve as kernel. The bandwidth is automatically selected
based on the plugin methodology (dpill
, see package
KernSmooth for more details).
prints only the requested value, without additional information.
prints all the additional information.
shows a picture visualizing a LCS
.
The result is an object of type qvalLCS
with the following entries:
smooth 
smoothing parameter 
feature 
feature parameter 
o 
xytable of observed values 
p 
xytable of predicted values 
obs 
xytable of (smoothed) observed values 
sim 
xytable of (smoothed) simulated values 
obsf 
interval sequence of observation according to selected 
simf 
interval sequence of simulation according to selected 
lcs 
output of 
obs.lcs 
one 
sim.lcs 
one 
Agrawal R., K. Lin., H. Sawhney and K. Shim (1995). Fast similarity search in the presence of noise, scaling, and translation in timeseries databases. In VLDB '95: Proceedings of the 21. International Conference on Very Large Data Bases, pp. 490501. Morgan Kaufmann Publishers Inc. ISBN 1558603794.
Cuberos F., J. Ortega, R. Gasca, M. Toro and J. Torres (2002). Qualitative comparison of temporal series  QSI. Topics in Artificial Intelligence. Lecture Notes in Artificial Intelligence, 2504, 7587.
Jachner, S., K.G. v.d. Boogaart, T. Petzoldt (2007) Statistical methods for the qualitative assessment of dynamic models with time delay (R package qualV), Journal of Statistical Software, 22(8), 1–30. doi: 10.18637/jss.v022.i08.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  # a constructed example
x < seq(0, 2*pi, 0.1)
y < 5 + sin(x) # a process
o < y + rnorm(x, sd=0.2) # observation with random error
p < y + 0.1 # simulation with systematic bias
qvalLCS(o, p)
qvalLCS(o, p, smooth="both", feature="f.curve")
qv < qvalLCS(o, p, smooth = "obs")
print(qv)
plot(qv, ylim=c(3, 8))
# observed and measured data with nonmatching time steps
data(phyto)
qvlcs < qvalLCS(obs$y, sim$y, obs$t, sim$t, smooth = "obs")
basedate < as.Date("1960/1/1")
qvlcs$o$x < qvlcs$o$x + basedate
qvlcs$obs$x < qvlcs$obs$x + basedate
qvlcs$sim$x < qvlcs$sim$x + basedate
qvlcs$obs.lcs$x < qvlcs$obs.lcs$x + basedate
qvlcs$sim.lcs$x < qvlcs$sim.lcs$x + basedate
plot.qvalLCS(qvlcs)
summary(qvlcs)

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