bbn.timeseries {bbnet}

Time Series Prediction with Bayesian Belief Network

Description

bbn.timeseries() performs time series predictions using a Bayesian Belief Network (BBN) model based on a single prior scenario. It generates figures illustrating how parameters change over time for all or selected nodes.

Usage

bbn.timeseries(bbn.model, priors1, timesteps = 5, disturbance = 1)

Arguments

bbn.model

A matrix or dataframe of interactions between different model nodes.

priors1

An X by 2 array of initial changes to the system under investigation. The first column should be a -4 to 4 (including 0) integer value for each node in the network with negative values indicating a decrease and positive values representing an increase. 0 represents no change.

timesteps

This is the number of timesteps the model performs. Default = 5. Note, timesteps are arbitrary and non-linear. However, something occurring in timestep 2, should occur before timestep 3.

disturbance

Default = 1. 1 creates a prolonged or press disturbance as per bbn.predict. Essentially prior values for each manipulated node are at least maintained (if not increased through reinforcement in the model) over all timesteps. 2 shows a brief pulse disturbance, which can be useful to visualise changes as peaks and troughs in increase and decrease of nodes can propagate through the network.

Value

Plots for each node showing the predicted change over time.

Examples

data(my_BBN, combined)
bbn.timeseries(bbn.model = my_BBN, priors1 = combined, timesteps=6, disturbance=1)