ASSA (version 2.0)

predict: Forecasting with Singular Spectrum Trendline

Description

Computes a forcasted trendline for time series data using singular spectrum analysis.

Usage

predict(fitted.model, p = 1)

Arguments

fitted.model

estimated model using the functions in the package.

p

the horizon to produce forecasts.

Value

forecast

Matrix containing in columns the dimensions and in rows the forecasts.

a

Parameters corresponding to the linear recurrence formula.

Details

predict is a wrapper function for predictions from the results of various singular spectrum model fitting functions on the package. The function invokes particular methods which depend on the class of the first argument (i.e. sst,msst, msstc, isst, misst, etc) .

References

de Carvalho, M. and Martos, G. (2020). Brexit: Tracking and disentangling the sentiment towards leaving the EU. International Journal of Forecasting, 36, 1128--1137. 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 sst,msst, msstc, isst, misst for a version of different models.

Examples

# NOT RUN {
## SIMULATED DATA EXAMPLE
set.seed(1)
N <- 500
t <- seq(.01, 5, length = N)
Y <- cbind(t^3 - 9 * t^2 + 23 * t + rnorm(N, 0, 1), 
           10 * sin(3 * t) / t + rnorm(N, 0, 1))
y <- mtsframe(date = t, Y)
fit <- msst(y)

# Forecasting: 
predict(fit, p = 5)$forecast
# }