simulate_hmm: Simulate data

In fHMM: Fitting Hidden Markov Models to Financial Data

Simulate data

Description

This helper function simulates HMM data.

Usage

simulate_hmm(
  controls = list(),
  hierarchy = FALSE,
  states = if (!hierarchy) 2 else c(2, 2),
  sdds = if (!hierarchy) "normal" else c("normal", "normal"),
  horizon = if (!hierarchy) 100 else c(100, 30),
  period = NA,
  true_parameters = fHMM_parameters(controls = controls, hierarchy = hierarchy, states =
    states, sdds = sdds),
  seed = NULL
)

Arguments

controls	Either a list or an object of class fHMM_controls. The list can contain the following elements, which are described in more detail below: hierarchy, defines an hierarchical HMM, states, defines the number of states, sdds, defines the state-dependent distributions, horizon, defines the time horizon, period, defines a flexible, periodic fine-scale time horizon, data, a list of controls that define the data, fit, a list of controls that define the model fitting Either none, all, or selected elements can be specified. Unspecified parameters are set to their default values. Important: Specifications in controls always override individual specifications.
hierarchy	A logical, set to TRUE for an hierarchical HMM. If hierarchy = TRUE, some of the other controls must be specified for the coarse-scale and the fine-scale layer. By default, hierarchy = FALSE.
states	An integer, the number of states of the underlying Markov chain. If hierarchy = TRUE, states must be a vector of length 2. The first entry corresponds to the coarse-scale layer, while the second entry corresponds to the fine-scale layer. By default, states = 2 if hierarchy = FALSE and states = c(2, 2) if hierarchy = TRUE.
sdds	A character, specifying the state-dependent distribution. One of "normal" (the normal distribution), "lognormal" (the log-normal distribution), "t" (the t-distribution), "gamma" (the gamma distribution), "poisson" (the Poisson distribution). The distribution parameters, i.e. the mean mu, standard deviation sigma (not for the Poisson distribution), degrees of freedom df (only for the t-distribution), can be fixed via, e.g., "t(df = 1)" or "gamma(mu = 0, sigma = 1)". To fix different values of a parameter for different states, separate by "|", e.g. "poisson(mu = 1|2|3)". If hierarchy = TRUE, sdds must be a vector of length 2. The first entry corresponds to the coarse-scale layer, while the second entry corresponds to the fine-scale layer. By default, sdds = "normal" if hierarchy = FALSE and sdds = c("normal", "normal") if hierarchy = TRUE.
horizon	A numeric, specifying the length of the time horizon. If hierarchy = TRUE, horizon must be a vector of length 2. The first entry corresponds to the coarse-scale layer, while the second entry corresponds to the fine-scale layer. By default, horizon = 100 if hierarchy = FALSE and horizon = c(100, 30) if hierarchy = TRUE. If data is specified (i.e., not NA), the first entry of horizon is ignored and the (coarse-scale) time horizon is defined by available data.
period	Only relevant if hierarchy = TRUE. In this case, a character which specifies a flexible, periodic fine-scale time horizon and can be one of "w" for a week, "m" for a month, "q" for a quarter, "y" for a year. By default, period = NA. If period is not NA, it overrules horizon[2].
true_parameters	An object of class fHMM_parameters, used as simulation parameters. By default, true_parameters = NULL, i.e., sampled true parameters.
seed	Set a seed for the data simulation. No seed per default.

Value

A list containing the following elements:

• time_points, the vector (or matrix in the hierarchical case) of time points,

• markov_chain, the vector (or matrix in the hierarchical case) of the simulated states,

• data, the vector (or matrix in the hierarchical case) of the simulated state-dependent observations,

• T_star, the numeric vector of fine-scale chunk sizes in the hierarchical case

Examples

simulate_hmm(states = 2, sdds = "normal", horizon = 10)

fHMM documentation built on April 3, 2025, 5:49 p.m.