fHMM (version 1.4.3)

fHMM-package: fHMM: Fitting Hidden Markov Models to Financial Data

Description

Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) tools:::Rd_expr_doi("10.1177/1471082X211034048") for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) tools:::Rd_expr_doi("10.18637/jss.v109.i09").

Author

Maintainer: Lennart Oelschläger (ORCID)

Authors:

• Timo Adam (ORCID)

• Rouven Michels (ORCID)

See Also

Useful links:

• https://loelschlaeger.de/fHMM/

• Report bugs at https://github.com/loelschlaeger/fHMM/issues

Examples

### 2-state HMM with normal distributions

# set specifications
controls <- set_controls(
  states = 2, sdds = "normal", horizon = 100, runs = 10
)

# define parameters
parameters <- fHMM_parameters(controls, mu = c(-1, 1), seed = 1)

# sample data
data <- prepare_data(controls, true_parameter = parameters, seed = 1)

# fit model
model <- fit_model(data, seed = 1)

# inspect fit
summary(model)
plot(model, "sdds")

# decode states
model <- decode_states(model)
plot(model, "ts")

# predict
predict(model, ahead = 5)