Compute regression coefficients of the model with minimum AIC by the least squares method via Householder transformation.
lsqr(y, lag = NULL, period = 365, plot = TRUE, ...)
An object of class "lsqr", which is a list with the following components:
"lsqr"
AIC's of the model with order \(0,\dots,k ( = 2\)lag\( + 1)\).
lag
residual variance of the model with order \(0,\dots,k\).
order of minimum AIC.
regression coefficients of the model.
trigonometric polynomial.
a univariate time series.
number of sine and cosine components. Default is \(\sqrt{n}\), where \(n\) is the length of the time series y.
y
period of one cycle.
logical. If TRUE (default), original data and fitted trigonometric polynomial are plotted.
TRUE
graphical arguments passed to plot.lsqr.
plot.lsqr
Kitagawa, G. (2020) Introduction to Time Series Modeling with Applications in R. Chapman & Hall/CRC.
# The daily maximum temperatures in Tokyo data(Temperature) lsqr(Temperature, lag = 10)
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