Description Usage Arguments Details Value References See Also Examples
Computes a trendline for multivariate interval data using singular spectrum analysis.
1 |
y |
object of class |
l |
window length; the string |
m |
number of leading eigentriples. An automatic
criterion based on the cumulative periodogram of the residuals is
provided by default by using the string |
vertical |
logical; if |
Multivariate singular spectrum analysis is used to decompose interval time
series data (y
) into principal components, and a cumulative
periodogram-based criterion automatically learns about what elementary
reconstructed components (erc
) contribute to the signal; see de
Carvalho and Martos (2018) for details. The trendline results from
adding elementary reconstructed components selected by the cumulative
periodogram of the residuals. The plot
method depicts the
trendlines, and the print
method reports the trendlines along
with the components selected by the cumulative periodogram-based
criterion.
trendline |
mitsframe object with interval trendline estimation from targeted grouping based on a cumulative periodogram criterion (or according to the number of components specified in vector |
l |
window length. |
m |
vector with number of components selected on each dimension. |
vertical |
flag indicating if the trajectory matrices where stacked vertically. |
residuals |
mitsframe object with the interval residuals from targeted grouping based on a cumulative periodogram criterion (or according to the number of components specified in vector |
svd |
the Singular Value Decomposition of the trajectory matrix. |
erc |
list with elementary reconstructed components. |
observations |
mitsframe object with the raw data |
de Carvalho, M. and Martos, G. (2020). Modeling Interval Trendlines: Symbolic Singular Spectrum Analysis for Interval Time Series. Submitted (available on arXiv).
See msst
for a similar routine yielding trendlines for
standard multivariate time series of data.
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 | muX.a = function(t){ 8 + t + sin(pi*t) } ; muX.b = function(t){ muX.a(t) + 2 }
muY.a = function(t){sqrt(t) + cos(pi*t/2) } ; muY.b = function(t){ 2*muY.a(t) + 2 }
N = 100; t=seq(0.1,2*pi,length = N);
set.seed(1)
e.x = rnorm(100); e.y = rnorm(100);
a.X = muX.a(t) + e.x; b.X = a.X + 2
a.Y = muY.a(t) + e.y ; b.Y = 2*a.Y + 2
A <- cbind(a.X, a.Y); B <- cbind(b.X, b.Y)
y <- mitsframe(dates=t, A=A, B = B)
fit <- misst(y)
fit$l;
fit$m;
fit$vertical
# Estimated trendlines:
head(fit$trendlines$A,5)
head(fit$trendlines$B,5)
## Estimated interval trendlines
plot(fit)
## Scree-plot
plot(fit, options = list(type = "screeplots"))
## Per
plot(fit, options = list(type = "cpgrams"))
## ERC
plot(fit, options=list(type='components',ncomp=1:3))
##################################
### Forecasting with misst ###
##################################
pred = predict(fit, p = 5)
pred$forecasts # Forecast organized in an array.
# End
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