Package 'OBL'

OBL: Optimal Block Length Compute Optimal Block Length for Non-overlapping, Overlapping, Circular Block, tapered moving, and tapered circular Block Bootstrap method

Description

OBL: Optimal Block Length

Compute Optimal Block Length for Non-overlapping, Overlapping, Circular Block, tapered moving, and tapered circular Block Bootstrap method

OBL: Optimal Block Length

Compute Optimal Block Length for Non-overlapping, Overlapping, Circular Block, tapered moving, and tapered circular Block Bootstrap method

Usage

blockboot(
  ts,
  R,
  seed,
  n_cores,
  methods = c("optnbb", "optmbb", "optcbb", "opttmbb", "opttcbb")
)

lolliblock(
  ts,
  R,
  seed,
  n_cores,
  methods = c("optnbb", "optmbb", "optcbb", "opttmbb", "opttcbb")
)

Arguments

ts	univariate time series
R	number of resample
seed	RNG seed
n_cores	number of core(s) to be used on your operaterating system
methods	"optnbb", "optmbb", "optcbb", "opttmbb", "opttcbb"

Value

A data frame get printed to the console

A data frame get printed to the console

Functions

• blockboot: package helps to obtain the optimal block length of a time series data

• lolliblock: package helps to obtain the optimal block length of a time series data

Examples

set.seed(289805)
  ts <- arima.sim(n = 3, model = list(ar = 0.8, order = c(1, 0, 0)), sd = 1)
  blockboot(ts = ts, R = 2, seed = 6, n_cores = 1)

  set.seed(289805)
  ts <- arima.sim(n = 3, model = list(ar = 0.8, order = c(1, 0, 0)), sd = 1)
  lolliblock(ts, R = 2, seed = 6, n_cores = 1)

---

Ten (10) simulated univaariate time series data.

Description

arima.sim returns the sum of all the values present in its arguments.

Usage

ts

Format

A time series data with 10 rows and 1 variables:

price

price, in US dollars

carat

weight of the diamond, in carats

...

Details

A dataset containing simulated univariate time series of 10 ts.

Value

It returns a univairate time series data It could be a vector

Source

Simulated data generated with the following code: set.seed(289805) ts <- stats::arima.sim(n = 10, model = list(ar = 0.8, order = c(1, 0, 0)), sd = 1)

Examples

set.seed(289805)
 ts <- stats::arima.sim(n = 10, model = list(ar = 0.8, order = c(1, 0, 0)), sd = 1)

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