Description Usage Arguments Details Value Author(s) References See Also Examples
Cluster time series with a common parametric trend using the
sync_test
function
\insertCiteLyubchich_Gel_2016_synchronism,Ghahari_etal_2017_MBDCEfuntimes.
1  sync_cluster(formula, rate = 1, alpha = 0.05, ...)

formula 
an object of class " 
rate 
rate of removal of time series. Default is 1 (i.e., if hypothesis of synchronism is rejected one time series is removed at a time to retest the remaining time series). Integer values above 1 are treated as number of time series to be removed. Values from 0 to 1 are treated as a fraction of time series to be removed. 
alpha 
significance level for testing hypothesis of a common trend
(using 
... 
arguments to be passed to 
The sync_cluster
function recursively clusters time series having
a prespecified common parametric trend until there are no time series left.
Starting with the given N time series, the sync_test
function
is used to test for a common trend. If null hypothesis of common trend is not
rejected by sync_test
, the time series are grouped together
(i.e., assigned to a cluster). Otherwise, the time series with the largest
contribution to the test statistics are temporarily removed (the number of time
series to remove depends on the rate
of removal) and sync_test
is applied again. The contribution to the test statistic is assessed by the
WAVK test statistic calculated for each time series.
A list with the elements:
cluster 
an integer vector indicating the cluster to which each time series is
allocated. A label 
elements 
a list with names of the time series in each cluster. 
The further elements combine results of sync_test
for each cluster with
at least two elements (that is, singleelement clusters labeled with
'0'
are excluded):
estimate 
a list with common parametric trend estimates obtained by

pval 
a list of pvalues of 
statistic 
a list with values of 
ar_order 
a list of AR filter orders used in 
window_used 
a list of local windows used in 
all_considered_windows 
a list of all windows considered in

WAVK_obs 
a list of WAVK test statistics obtained in 
Srishti Vishwakarma, Vyacheslav Lyubchich
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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47  ## Not run:
## Simulate 4 autoregressive time series,
## 3 having a linear trend and 1 without a trend:
set.seed(123)
T = 100 #length of time series
N = 4 #number of time series
X = sapply(1:N, function(x) arima.sim(n = T,
list(order = c(1, 0, 0), ar = c(0.6))))
X[,1] < 5 * (1:T)/T + X[,1]
plot.ts(X)
# Finding clusters with common linear trends:
LinTrend < sync_cluster(X ~ t)
## Sample Output:
##[1] "Cluster labels:"
##[1] 0 1 1 1
##[1] "Number of singleelement clusters (labeled with '0'): 1"
## plotting the time series of the cluster obtained
for(i in 1:max(LinTrend$cluster)) {
plot.ts(X[, LinTrend$cluster == i],
main = paste("Cluster", i))
}
## Simulating 7 autoregressive time series,
## where first 4 time series have a linear trend added
set.seed(234)
T = 100 #length of time series
a < sapply(1:4, function(x) 10 + 0.1 * (1:T) +
arima.sim(n = T, list(order = c(1, 0, 0), ar = c(0.6))))
b < sapply(1:3, function(x) arima.sim(n = T,
list(order = c(1, 0, 0), ar = c(0.6))))
Y < cbind(a, b)
plot.ts(Y)
## Clustering based on linear trend with rate of removal = 2
# and confidence level for the synchronism test 90%
LinTrend7 < sync_cluster(Y ~ t, rate = 2, alpha = 0.1, B = 99)
## Sample output:
##[1] "Cluster labels:"
##[1] 1 1 1 0 2 0 2
##[1] "Number of singleelement clusters (labeled with '0'): 2"
## End(Not run)

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