polreg

0th

Percentile

Polynomial Regression Model

Estimate the trend using the AIC best polynomial regression model.

Keywords
ts
Usage
polreg(y, order, plot = TRUE, …)
Arguments
y

a univariate time series.

order

order of polynomial regression.

plot

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

further arguments to be passed to plot.polreg.

Value

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

order.maice

MAICE (minimum AIC estimate) order.

sigma2

residual variance of the model with order \(M\). (\(0 \leq M \leq\) order \(+ 1\))

aic

AIC of the model with order \(M\). (\(0 \leq M \leq\) order \(+ 1\))

daic

AIC - minimum AIC.

coef

regression coefficients \(A(I,M)\) with order \(M\).

(\(1 \leq M \leq\) order \(+ 1\), \(1 \leq I \leq M\))

trend

trend component.

References

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

Aliases
  • polreg
Examples
# NOT RUN {
# The daily maximum temperatures for Tokyo
data(Temperature)
polreg(Temperature, order = 7)

# Wholesale hardware data
data(WHARD)
y <- log10(WHARD)
polreg(y, order = 15)
# }
Documentation reproduced from package TSSS, version 1.2.3, License: GPL (>= 2)

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