# lsqr

From TSSS v1.2.3
by Masami Saga

##### The Least Squares Method via Householder Transformation

Compute Regression coefficients of the model with minimum AIC.

- Keywords
- ts

##### 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's of the model with order \(0,\dots,k ( = 2\)`lag`

\( + 1)\).

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

order of minimum AIC.

regression coefficients of the model.

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)
# }
```

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

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