fssa(Y, L = NA, type = "fssa")

## Not run: 
## Univariate FSSA Example on Callcenter data
data("Callcenter")
require(fda)
require(Rfssa)
## Define functional objects
D <- matrix(sqrt(Callcenter$calls),nrow = 240)
N <- ncol(D)
time <- seq(ISOdate(1999,1,1), ISOdate(1999,12,31), by="day")
K <- nrow(D)
u <- seq(0,K,length.out =K)
d <- 22 #Optimal Number of basis elements
basis <- create.bspline.basis(c(min(u),max(u)),d)
Ysmooth <- smooth.basis(u,D,basis)
## Define functional time series
Y <- fts(Ysmooth$fd,time = time)
plot(Y,ylab = "Sqrt of Callcenter", xlab = "Intraday intervals")

## Univariate functional singular spectrum analysis
L <- 28
U <- fssa(Y,L)
plot(U,d=13)
plot(U,d=9,type="lheats")
plot(U,d=9,type="lcurves")
plot(U,d=9,type="vectors")
plot(U,d=10,type="periodogram")
plot(U,d=10,type="paired")
plot(U,d=10,type="wcor")
gr <- list(1,2:3,4:5,6:7,8:20)
Q <- freconstruct(U, gr)
plot(Y,main="Call Numbers(Observed)")
plot(Q[[1]],main="1st Component",ylab = " ", xlab = "Intraday intervals")
plot(Q[[2]],main="2nd Component",ylab = " ", xlab = "Intraday intervals")
plot(Q[[3]],main="3rd Component",ylab = " ", xlab = "Intraday intervals")
plot(Q[[4]],main="4th Component",ylab = " ", xlab = "Intraday intervals")
plot(Q[[5]],main="5th Component(Noise)",ylab = " ", xlab = "Intraday intervals")

## Other visiualisation types for object of class "fts":

plot(Q[[1]], type="3Dsurface", main="1st Component",ylab = " ", xlab = "Intraday intervals")
plot(Q[[2]][1:60], type="heatmap", main="2nd Component",ylab = " ", xlab = "Intraday intervals")
plot(Q[[3]][1:60], type = "3Dline", main="3rd Component",ylab = " ", xlab = "Intraday intervals")

## Multivariate FSSA Example on Bivariate Satelite Image Data
require(fda)
require(Rfssa)
## Raw image data
NDVI=Jambi$NDVI
EVI=Jambi$EVI
time <- Jambi$Date
## Kernel density estimation of pixel intensity
D0_NDVI <- matrix(NA,nrow = 512, ncol = 448)
D0_EVI <- matrix(NA,nrow =512, ncol = 448)
for(i in 1:448){
  D0_NDVI[,i] <- density(NDVI[,,i],from=0,to=1)$y
  D0_EVI[,i] <- density(EVI[,,i],from=0,to=1)$y
}
## Define functional objects
d <- 11
basis <- create.bspline.basis(c(0,1),d)
u <- seq(0,1,length.out = 512)
y_NDVI <- smooth.basis(u,as.matrix(D0_NDVI),basis)$fd
y_EVI <- smooth.basis(u,as.matrix(D0_EVI),basis)$fd
y=list(y_NDVI,y_EVI)
## Define functional time series
Y <- fts(y,time=time)
plot(Y[1:100],ylab = c("NDVI","EVI"),main = "Probability Kernel Density")
plot(Y, type = '3Dsurface', var=1,ylab = c("NDVI"),main = "Probability Kernel Density")
plot(Y, type = '3Dline', var=2,ylab = c("EVI"),main = "Probability Kernel Density")
plot(Y, type = 'heatmap',ylab = c("NDVI","EVI"),main = "Probability Kernel Density")
L=45
## Multivariate functional singular spectrum analysis
U=fssa(Y,L)
plot(U,d=10,type='values')
plot(U,d=10,type='paired')
plot(U,d=10,type='lheats', var = 1)
plot(U,d=10,type='lcurves',var = 1)
plot(U,d=10,type='lheats', var = 2)
plot(U,d=10,type='lcurves',var = 2)
plot(U,d=10,type='wcor')
plot(U,d=10,type='periodogram')
plot(U,d=10,type='vectors')
recon <- freconstruct(U = U, group = list(c(1),c(2,3),c(4)))
plot(recon[[1]],type = '3Dsurface',var=1, ylab = "NDVI")
plot(recon[[2]],type = '3Dsurface',var=1, ylab = "NDVI")
plot(recon[[3]],type = '3Dsurface',var=1, ylab = "NDVI")
plot(recon[[1]],type = '3Dsurface',var=2, ylab = "EVI")
plot(recon[[2]],type = '3Dsurface',var=2, ylab = "EVI")
plot(recon[[3]],type = '3Dsurface',var=2, ylab = "EVI")


## End(Not run)