`fastcpd_ts`

is a wrapper function for `fastcpd`

to find
change points in time series data. The function is similar to `fastcpd`

except that the data is a time series data and the family is one of
`"ar"`

, `"var"`

, `"arima"`

or `"garch"`

.

## Arguments

- data
A numeric vector, a matrix, a data frame or a time series object.

- family
A character string specifying the family of the time series. The value should be one of

`"ar"`

,`"var"`

,`"arima"`

or`"garch"`

.- order
A positive integer or a vector of length less than four specifying the order of the time series. Possible combinations with

`family`

are:ar, NUMERIC(1): AR(p) model using linear regression.

ar, NUMERIC(3): ARIMA(p, 0, 0) model using

`forecast::Arima`

, where`p`

is the first element of the vector.var, NUMERIC(1): VAR(p) model using linear regression.

ma, NUMERIC(1): MA(q) model using

`forecast::Arima`

.ma, NUMERIC(3): ARIMA(0, 0, q) model using

`forecast::Arima`

, where`q`

is the third element of the vector.arima, NUMERIC(3): ARIMA(p, d, q) model using

`forecast::Arima`

.garch, NUMERIC(2): GARCH(p, q) model using

`tseries::garch`

.

- ...
Other arguments passed to

`fastcpd`

, for example,`segment_count`

. One special argument can be passed here is`include.mean`

, which is a logical value indicating whether the mean should be included in the model. The default value is`TRUE`

.

## Examples

```
# \donttest{
set.seed(1)
n <- 600
x <- rep(0, n + 1)
for (i in 1:300) {
x[i + 1] <- 0.8 * x[i] + rnorm(1, 0, 2)
}
for (i in 301:n) {
x[i + 1] <- 0.1 * x[i] + rnorm(1, 0, 2)
}
result <- fastcpd.ts(
x[1 + seq_len(n)],
"ar",
c(1, 0, 0),
include.mean = FALSE,
trim = 0,
beta = (1 + 1 + 1) * log(n) / 2 * 3
)
summary(result)
#>
#> Call:
#> fastcpd.ts(data = x[1 + seq_len(n)], family = "ar", order = c(1,
#> 0, 0), include.mean = FALSE, trim = 0, beta = (1 + 1 + 1) *
#> log(n)/2 * 3)
#>
#> Change points:
#> 301
#>
#> Cost values:
#> 623.7791 643.8769
plot(result)
# }
```