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` |
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 |

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 |

`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 `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 EXAMPLE ## (de Carvalho and Martos, 2018; Fig. 1) data(brexit) attach(brexit) y <- mtsframe(date, brexit[, 1:3] / 100) fit <- msstc(y) # Estimations on the simplex rowSums(fit$trendlines$Y) # Forecast also in the simplex rowSums(predict(fit, p = 5)$forecast) ## Window length and components automatically selected fit$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") ) ## Scree-plot (with 95% confidence bands) 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 ## (de Carvalho and Martos, 2020; Fig. 5) plot(fit, options = list(type = "components", ncomp = 1:2))

[Package *ASSA* version 2.0 Index]