msst {ASSA} | R Documentation |

Computes trendlines for multivariate time series data using multivariate singular spectrum analysis.

msst(y, l = "automatic", m = "automatic", vertical = TRUE)

`y` |
mtsframe object containing raw data. |

`l` |
window length; the string |

`m` |
vector with the number of leading eigentriples on each dimension. 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 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` |
mtsframe object with 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` |
mtsframe object with the 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` |
mtsframe object with the observations |

Gabriel Martos and Miguel de Carvalho

de Carvalho, M. and Martos, G. (2020). Brexit: Tracking and
disentangling the sentiment towards leaving the EU. *International Journal
of Forecasting*, **36**, 1128–1137.

See `msstc`

for a similar routine yielding trendlines for
multivariate time series of compositional data.

## SIMULATED EXAMPLE t <- seq(0.05, 5, by = 0.05) t2 <- seq(0.05, 6, by = 0.05) p = length(t2)-length(t) # Forecasting horizon parameter: n = length(t) Y <- cbind(t^3 - 9 * t^2 + 23 * t + rnorm(n, 0, 1), 10 * sin(3 * t) / t + rnorm(n, 0, 1)) y <- mtsframe(dates = t, Y) fit.vertical <- msst(y) pred.vertical <- predict(fit.vertical, p = p) print(pred.vertical$forecast) ## BREXIT DATA EXAMPLE ## (de Carvalho and Martos, 2018; Fig. 1) data(brexit) attach(brexit) y <- mtsframe(date, brexit[, 1:3] / 100) fit <- msst(y) ## Window length and components automatically selected fit$l; fit$m ## 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 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 par(mfrow = c(1, 1)) plot(fit, options = list(type = "screeplot", ncomp.scree = 1:10), type = "b", pch = 20, lwd = 2, main='Scree plot') ## Plot elementary reconstructed components plot(fit, options = list(type = "components", ncomp = 1:2))

[Package *ASSA* version 2.0 Index]