isst {ASSA} | R Documentation |
Interval Singular Spectrum Trendlines
Description
Computes the trendline estimation for interval time series data using singular spectrum analysis.
Usage
isst(y, l= 'automatic' , m = 'automatic')
Arguments
y |
|
l |
window length; the string |
m |
number of leading eigentriples; the string |
Details
Singular spectrum analysis decompose time series data (y
)
into principal components, and a cumulative periodogram-based
criterion learn about elementary reconstructed components
(erc
) that contribute to the signal. The trendline results
from adding principal components selected by a cumulative
periodogram-based criteria; see de Carvalho and Martos (2018,
Section 4.1). The plot
method yields the resulting trendlines
along with the data; options
for the plot method are give by
a list including the strings "trendline"
,
"components"
, "cpgram"
, and "screeplot"
,
along with a set of values (ncomp
) indicating the components
on which these diagnostics are to be depicted (e.g. plot(fit,
options = list(type = "components", ncomp = 1:3))
.
Value
trendline |
itsframe object with interval trendline estimation from
targeted grouping based on a cumulative periodogram criterion (or
according to the number of components specified in |
l |
window length. |
m |
number of leading eigentriples. An automatic
criterion based on the cumulative periodogram of the residuals is
provided by default by using the string |
residuals |
itsframe object with the residuals from targeted
grouping based on a cumulative periodogram criterion (or according
to the number of components specified in |
svd |
Singular value decomposition corresponding to the trajectory matrix. |
erc |
elementary reconstructed components. |
observations |
itsframe object with the raw data. |
References
de Carvalho, M. and Martos, G. (2020). Modeling Interval Trendlines: Symbolic Singular Spectrum Analysis for Interval Time Series. Submitted (available on arXiv).
See Also
See misst
for a version of the routine for multivariate
interval value time series.
Examples
# Merval data example:
data(merval)
id.data <- rev(which(merval[,1]>'2015-12-31' & merval[,1]<'2020-10-01') )
y <- itsframe(date=merval[id.data,1], a=merval[id.data,2], b=merval[id.data,3]);
isst_output <- isst(y ,l = 'automatic', m = 'automatic')
print(isst_output)
# Estimated trendlines
plot(isst_output)
## Scree-plot
plot(isst_output, options = list(type = "screeplot", ncomp = 1:10),
type = "b", pch = 20, lwd = 2)
# Elementary reconstructed components
plot(isst_output, options=list(type='components',ncomp=1:3),
xlab='Time')
# cpgram's ('a=low' and 'b=high')
plot(isst_output, options = list(type='cpgram'))
# Setting m = 'automatic' (default option) to obtain cpgrams inside the bandwiths.
##################################
### Forecasting with isst ###
##################################
pred <- predict(isst_output, p = 5)
head(pred$forecast,3) # Forecasted interval data.
attributes(pred)
pred$coefficients[1:5] # linear recurrence parameters.
# End