msstc {ASSA} | R Documentation |
Computes trendlines on the unit simplex for multivariate time series data using multivariate singular spectrum analysis.
msstc(y, l = 'automatic', m = 'automatic', vertical = TRUE)
y |
mtsframe object containing data. |
l |
window length; the string |
m |
number of leading eigentriples; the string |
vertical |
logical; if |
The trendline produced using this routine is based on the methods
proposed in de Carvalho and Martos (2018). A quick summary of the
method is as follows. Multivariate singular spectrum analysis is
used to decompose 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, and
after projecting into the unit simplex. 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 |
mtsframe object with trendline estimation from
targeted grouping based on a cumulative periodogram criterion (or
according to the number of components specified in vector
|
residuals |
mtsframe object with the residuals from targeted
grouping based on a cumulative periodogram criterion (or according
to the number of components specified in vector |
erc |
list with elementary reconstructed components. |
eigen.val |
vector with the singular values of the trajectory matrix. |
l |
window length. |
selected.components |
vector with number of components selected on each dimension. |
selection.criteria |
a vector indicating if the null hypothesis of white noise is rejected along the dimensions (0: not rejected, 1: rejected). |
rank |
rank of the trajectory matrix. |
Gabriel Martos and Miguel de Carvalho
de Carvalho, M. and Martos, G. (2018). Brexit: Tracking and disentangling the sentiment towards leaving the EU. Submitted.
See msst
for a similar routine yielding trendlines for
multivariate time series, but which does not project the pointwise
estimates to the unit simplex.
## Brexit data and MSSA on the simplex ## (de Carvalho and Martos, 2018; Fig. 1) data(brexit) attach(brexit) y <- mtsframe(date, brexit[, 1:3] / 100) fit <- msstc(y) ## Window length and number of components automatically selected on ## each dimension: fit$l fit$selected.components ## Plot trendlines (de Carvalho and Martos, 2018; Fig. 1) plot(fit, options = list(type = "trendlines"), xlab="time", col=c("blue", "red", "black"), lwd = 2, lty = c(1, 2, 3)) ## Plot elementary reconstructed components ## (de Carvalho and Martos, 2018; Fig. 5) plot(fit, options = list(type = "components", ncomp = 1:3)) ## Plot cumulative periodograms (with 95% confidence bands) par(mfrow = c(1, 3)) plot(fit, options = list(type = "cpgrams", series.names = c('Leave','Stay','Undecided')) ) ## Scree-plot (with 95% confidence bands) par(mfrow = c(1, 1)) plot(fit, options = list(type = "screeplots", ncomp = 1:10), type = "b", pch = 20, lwd = 2, main='Scree plot')