ts_brownian_motion_augment: Brownian Motion

In healthyR.ts: The Time Series Modeling Companion to 'healthyR'

Brownian Motion

Description

Create a Brownian Motion Tibble

Usage

ts_brownian_motion_augment(
  .data,
  .date_col,
  .value_col,
  .time = 100,
  .num_sims = 10,
  .delta_time = NULL
)

Arguments

.data	The data.frame/tibble being augmented.
.date_col	The column that holds the date.
.value_col	The value that is going to get augmented. The last value of this column becomes the initial value internally.
.time	How many time steps ahead.
.num_sims	How many simulations should be run.
.delta_time	Time step size.

Details

Brownian Motion, also known as the Wiener process, is a continuous-time random process that describes the random movement of particles suspended in a fluid. It is named after the physicist Robert Brown, who first described the phenomenon in 1827.

The equation for Brownian Motion can be represented as:

W(t) = W(0) + sqrt(t) * Z

Where W(t) is the Brownian motion at time t, W(0) is the initial value of the Brownian motion, sqrt(t) is the square root of time, and Z is a standard normal random variable.

Brownian Motion has numerous applications, including modeling stock prices in financial markets, modeling particle movement in fluids, and modeling random walk processes in general. It is a useful tool in probability theory and statistical analysis.

Value

A tibble/matrix

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Data Generator: tidy_fft(), ts_brownian_motion(), ts_geometric_brownian_motion_augment(), ts_geometric_brownian_motion(), ts_random_walk()

Examples

rn <- rnorm(31)
df <- data.frame(
 date_col = seq.Date(from = as.Date("2022-01-01"),
                      to = as.Date("2022-01-31"),
                      by = "day"),
 value = rn
)

ts_brownian_motion_augment(
  .data = df,
  .date_col = date_col,
  .value_col = value
)

healthyR.ts documentation built on Nov. 15, 2023, 9:07 a.m.