Time Series Forecasting Using Caret Infrastructure
person(given = "Resul",
family = "Akay",
role = c("aut", "cre"),
email = "resulakay1@gmail.com")
Recursive time series forecast using Caret infrastructure.
The models are selected based on time series cross-validation and
forecasting is done recursively.

Conformal Time Series Forecasting Using State of Art Machine
Learning Algorithms
Conformal time series forecasting using the caret infrastructure.
It provides access to state-of-the-art machine learning models for forecasting
applications. The hyperparameter of each model is selected based on time
series cross-validation, and forecasting is done recursively.

Classification and Regression Training LSF Style
Augment some caret functions for parallel processing

Classification and Regression Training in Parallel Using NetworkSpaces
Augment some caret functions using parallel processing

Continuous-Time Fractionally Integrated ARMA Process for
Irregularly Spaced Long-Memory Time Series Data
We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via frequentist or Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005)<doi:10.1111/j.1467-9868.2005.00522.x> and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces their posterior distributions via Metropolis within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo for posterior sampling. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.

c(person("Hyungsuk","Tak",role="aut",email="hyungsuk.tak@gmail.com",comment=c(ORCID="0000-0003-0334-8742")),
person("Henghsiu", "Tsai", role = "aut", email="henghsiu.tsai@gmail.com"),
person("Kisung", "You", role = c("aut", "cre"),email = "kyou@nd.edu",comment=c(ORCID="0000-0002-8584-459X")))
We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via frequentist or Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) <doi:10.1111/j.1467-9868.2005.00522.x> and it involves (p+q+2) unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces their posterior distributions via Metropolis within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo for posterior sampling. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.
Rcpp, DEoptim, Rdpack, MASS, cubature, numDeriv, stats, utils
Hyungsuk Tak [aut] (<https://orcid.org/0000-0003-0334-8742>),
Henghsiu Tsai [aut],
Kisung You [aut, cre] (<https://orcid.org/0000-0002-8584-459X>)