polreg: Polynomial Regression Model

In TSSS: Time Series Analysis with State Space Model

Polynomial Regression Model

Description

Estimate the trend using the AIC best polynomial regression model.

Usage

polreg(y, order, plot = TRUE, ...)

Arguments

y	a univariate time series.
order	maximum order of polynomial regression.
plot	logical. If TRUE (default), original data and trend component are plotted.
...	graphical arguments passed to plot.polreg.

Value

An object of class "polreg", which is a list with the following components:

order.maice	MAICE (minimum AIC estimate) order.
sigma2	residual variance of the model with order M. (0 \leq M \leq order)
aic	AIC of the model with order M. (0 \leq M \leq order)
daic	AIC - minimum AIC.
coef	regression coefficients A(I,M) with order M. (1 \leq M \leq order, 1 \leq I \leq M)
trend	trend component.

References

Kitagawa, G. (2020) Introduction to Time Series Modeling with Applications in R. Chapman & Hall/CRC.

Examples

# The daily maximum temperatures for Tokyo
data(Temperature)
polreg(Temperature, order = 7)

# Wholesale hardware data
data(WHARD)
y <- log10(WHARD)
polreg(y, order = 15)

TSSS documentation built on Sept. 29, 2023, 9:07 a.m.