Data Generation


Data generation or simulation is a way to get more time series data from either a small data set or a parametric simulating model. Data generation in Bruno uses a DataGenInput subtype as an input into the makedata function.

Creating a Simulated Time Series

This example shows how to make a Julia matrix with two simulated time series using the log diffusion parameteric model (discrete geometric Brownian motion). To use other data generation models check the reference for all current data generation inputs.

# creating a LogDiffInput struct with input parameters
input = LogDiffInput(; 

# creating 2 new timeseries
timeseries = makedata(input, 2)