trend

0th

Percentile

Trend Estimation

Estimate the trend by state space model.

Keywords
ts
Usage
trend(y, trend.order = 1, tau2.ini = NULL, delta, plot = TRUE, …)
Arguments
y

a univariate time series.

trend.order

trend order.

tau2.ini

initial estimate of variance of the system noise $\tau^2$. If tau2.ini = NULL, the most suitable value is chosen in $\tau^2 = 2^{-k}$.

delta

search width (for tau2.ini is specified (not NULL)) .

plot

logical. If TRUE (default), 'trend' and 'residual' are plotted.

further arguments to be passed to plot.trend.

Details

The trend model can be represented by a state space model

$$x_n = Fx_{n-1} + Gv_n,$$ $$y_n = Hx_n + w_n,$$

where $F$, $G$ and $H$ are matrices with appropriate dimensions. We assume that $v_n$ and $w_n$ are white noises that have the normal distributions $N(0,\tau^2)$ and $N(0, \sigma^2)$, respectively.

Value

An object of class "trend", which is a list with the following elements:

trend

trend component.

residual

residuals.

tau2

variance of the system noise $\tau^2$.

sigma2

variance of the observational noise $\sigma^2$.

llkhood

log-likelihood of the model.

aic

AIC.

References

Kitagawa, G. (2010) Introduction to Time Series Modeling. Chapman & Hall/CRC.

• trend
Examples
# NOT RUN {
# The daily maximum temperatures for Tokyo
data(Temperature)
trend(Temperature, trend.order = 1, tau2.ini = 0.223, delta = 0.001)

trend(Temperature, trend.order = 2)
# }

Documentation reproduced from package TSSS, version 1.2.3, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.