# Exploring Random Walks and Brownian Motions with healthyR.ts

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# Introduction

In the world of time series analysis, Random Walks, Brownian Motion, and Geometric Brownian Motion are fundamental concepts used in various fields, including finance, physics, and biology. Today, we’ll explore these concepts using functions from the `healthyR.ts`

package.

## Random Walks

A Random Walk is a path that consists of a series of random steps. It’s a simple but powerful concept used to model seemingly unpredictable paths, such as stock prices or animal movements.

Let’s generate and plot some Random Walks using the `ts_random_walk()`

function from `healthyR.ts`

.

### Function Syntax

ts_random_walk( .mean = 0, .sd = 0.1, .num_walks = 100, .periods = 100, .initial_value = 1000 )

`.mean`

: The desired mean of the random walks.`.sd`

: The standard deviation of the random walks.`.num_walks`

: The number of random walks you want to generate.`.periods`

: The length of the random walk(s) you want to generate.`.initial_value`

: The initial value where the random walks should start.

### Example

library(ggplot2) library(healthyR.ts) random_walk_data <- ts_random_walk( .mean = 0, .sd = 0.1, .num_walks = 10, .periods = 100, .initial_value = 1000 ) head(random_walk_data)

# A tibble: 6 × 4 run x y cum_y <dbl> <dbl> <dbl> <dbl> 1 1 1 0.0725 1072. 2 1 2 0.182 1267. 3 1 3 0.110 1407. 4 1 4 0.0275 1445. 5 1 5 -0.0546 1367. 6 1 6 0.0712 1464.

random_walk_plot <- random_walk_data |> ggplot( mapping = aes( x = x, y = cum_y, color = factor(run), group = factor(run) ) ) + geom_line(alpha = 0.8) + ts_random_walk_ggplot_layers(random_walk_data) print(random_walk_plot)

This code generates 10 random walks over 100 periods, starting from an initial value of 1000. The resulting plot visualizes the paths of these random walks, each represented by a different color.

## Brownian Motion

Brownian Motion, also known as Wiener Process, is a continuous-time stochastic process that is often used to model random movements in physics and finance.

### Function Syntax

ts_brownian_motion( .time = 100, .num_sims = 10, .delta_time = 1, .initial_value = 0, .return_tibble = TRUE )

`.time`

: Total time of the simulation.`.num_sims`

: Total number of simulations.`.delta_time`

: Time step size.`.initial_value`

: Initial value of the simulation.`.return_tibble`

: Return a tibble (TRUE) or a matrix (FALSE).

### Example

brownian_data <- ts_brownian_motion( .time = 100, .num_sims = 10, .delta_time = 1, .initial_value = 0, .return_tibble = TRUE ) head(brownian_data)

# A tibble: 6 × 3 sim_number t y <fct> <int> <dbl> 1 sim_number 1 1 0 2 sim_number 2 1 0 3 sim_number 3 1 0 4 sim_number 4 1 0 5 sim_number 5 1 0 6 sim_number 6 1 0

brownian_plot <- ts_brownian_motion_plot( .data = brownian_data, .date_col = t, .value_col = y, .interactive = TRUE ) brownian_plot

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