TSSS (version 1.2.3)

lsqr: The Least Squares Method via Householder Transformation

Description

Compute Regression coefficients of the model with minimum AIC.

Usage

lsqr(y, lag = 10, plot = TRUE, …)

Arguments

y

a univariate time series.

lag

number of sine and cosine terms.

plot

logical. If TRUE (default), original data and fitted trigonometric polynomial are plotted.

…

further arguments to be passed to plot.lsqr.

Value

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

aic

AIC's of the model with order \(0,\dots,k ( = 2\)lag\( + 1)\).

sigma2

residual variance of the model with order \(0,\dots,k\).

maice.order

order of minimum AIC.

regress

regression coefficients of the model.

tripoly

trigonometric polynomial.

References

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

Examples

# NOT RUN {
# The daily maximum temperatures for Tokyo
data(Temperature)
lsqr(Temperature)
# }