R/fitTBATS.R

In forecast: Forecasting Functions for Time Series and Linear Models

fitPreviousTBATSModel <- function(y, model, biasadj = FALSE) {
  seasonal.periods <- model$seasonal.periods
  if (!is.null(seasonal.periods)) {
    seasonal.periods <- sort(seasonal.periods)
  }
  # Get the parameters out of the param.vector
  paramz <- unParameteriseTBATS(model$parameters$vect, model$parameters$control)
  lambda <- paramz$lambda
  alpha <- paramz$alpha
  beta.v <- paramz$beta
  if (!is.null(beta.v)) {
    adj.beta <- 1
  } else {
    adj.beta <- 0
  }
  small.phi <- paramz$small.phi
  gamma.one.v <- paramz$gamma.one.v
  gamma.two.v <- paramz$gamma.two.v
  if (!is.null(paramz$ar.coefs)) {
    p <- length(paramz$ar.coefs)
    ar.coefs <- matrix(paramz$ar.coefs, nrow = 1, ncol = p)
  } else {
    ar.coefs <- NULL
    p <- 0
  }
  if (!is.null(paramz$ma.coefs)) {
    q <- length(paramz$ma.coefs)
    ma.coefs <- matrix(paramz$ma.coefs, nrow = 1, ncol = q)
  } else {
    ma.coefs <- NULL
    q <- 0
  }

  if (!is.null(seasonal.periods)) {
    tau <- as.integer(2 * sum(model$k.vector))
    gamma.bold <- matrix(0, nrow = 1, ncol = (2 * sum(model$k.vector)))
  } else {
    tau <- 0L
    gamma.bold <- NULL
  }

  g <- matrix(
    0,
    nrow = ((2 * sum(model$k.vector)) + 1 + adj.beta + p + q),
    ncol = 1
  )
  if (p != 0) {
    g[(1 + adj.beta + tau + 1), 1] <- 1
  }
  if (q != 0) {
    g[(1 + adj.beta + tau + p + 1), 1] <- 1
  }

  y.touse <- y
  if (!is.null(lambda)) {
    y.touse <- BoxCox(y, lambda = lambda)
    lambda <- attr(y.touse, "lambda")
  }

  ## Calculate the variance:
  # 1. Re-set up the matrices
  w <- makeTBATSWMatrix(
    smallPhi = small.phi,
    kVector = model$k.vector,
    arCoefs = ar.coefs,
    maCoefs = ma.coefs,
    tau = tau
  )
  if (!is.null(gamma.bold)) {
    updateTBATSGammaBold(
      gammaBold = gamma.bold,
      kVector = model$k.vector,
      gammaOne = gamma.one.v,
      gammaTwo = gamma.two.v
    )
  }
  updateTBATSGMatrix(
    g = g,
    gammaBold = gamma.bold,
    alpha = alpha,
    beta = beta.v
  )
  F <- makeTBATSFMatrix(
    alpha = alpha,
    beta = beta.v,
    small.phi = small.phi,
    seasonal.periods = seasonal.periods,
    k.vector = model$k.vector,
    gamma.bold.matrix = gamma.bold,
    ar.coefs = ar.coefs,
    ma.coefs = ma.coefs
  )
  updateFMatrix(
    F,
    small.phi,
    alpha,
    beta.v,
    gamma.bold,
    ar.coefs,
    ma.coefs,
    tau
  )
  # 2. Calculate!
  fitted.values.and.errors <- calcModel(y.touse, model$seed.states, F, g, w)
  e <- fitted.values.and.errors$e
  fitted.values <- fitted.values.and.errors$y.hat
  variance <- sum((e * e)) / length(y)
  if (!is.null(lambda)) {
    fitted.values <- InvBoxCox(
      fitted.values,
      lambda = lambda,
      biasadj,
      variance
    )
  }

  model.for.output <- model
  model.for.output$variance <- variance
  model.for.output$fitted.values <- ts(c(fitted.values))
  model.for.output$errors <- ts(c(e))
  tsp(model.for.output$fitted.values) <- tsp(model.for.output$errors) <- tsp(y)
  model.for.output$x <- fitted.values.and.errors$x
  model.for.output$y <- y
  model.for.output
}

fitSpecificTBATS <- function(
  y,
  use.box.cox,
  use.beta,
  use.damping,
  seasonal.periods = NULL,
  k.vector = NULL,
  starting.params = NULL,
  x.nought = NULL,
  ar.coefs = NULL,
  ma.coefs = NULL,
  init.box.cox = NULL,
  bc.lower = 0,
  bc.upper = 1,
  biasadj = FALSE
) {
  if (!is.null(seasonal.periods)) {
    seasonal.periods <- sort(seasonal.periods)
  }
  ## Meaning/purpose of the first if() statement: If this is the first pass, then use default starting values. Else if it is the second pass, then use the values form the first pass as starting values.
  if (is.null(starting.params)) {
    ## Check for the existence of ARMA() coefficients
    if (!is.null(ar.coefs)) {
      p <- length(ar.coefs)
    } else {
      p <- 0
    }
    if (!is.null(ma.coefs)) {
      q <- length(ma.coefs)
    } else {
      q <- 0
    }
    # Calculate starting values:
    alpha <- 0.09
    if (use.beta) {
      adj.beta <- 1
      beta.v <- 0.05
      b <- 0.00
      if (use.damping) {
        small.phi <- .999
      } else {
        small.phi <- 1
      }
    } else {
      adj.beta <- 0
      beta.v <- NULL
      b <- NULL
      small.phi <- NULL
      use.damping <- FALSE
    }
    if (!is.null(seasonal.periods)) {
      gamma.one.v <- rep(0, length(k.vector))
      gamma.two.v <- rep(0, length(k.vector))
      s.vector <- numeric(2 * sum(k.vector))
      k.vector <- as.integer(k.vector)
    } else {
      gamma.one.v <- NULL
      gamma.two.v <- NULL
      s.vector <- NULL
    }
    if (use.box.cox) {
      if (!is.null(init.box.cox)) {
        lambda <- init.box.cox
      } else {
        lambda <- BoxCox.lambda(y, lower = 0, upper = 1.5)
      }
      y.transformed <- BoxCox(y, lambda = lambda)
      lambda <- attr(y.transformed, "lambda")
    } else {
      # the "else" is not needed at the moment
      lambda <- NULL
    }
  } else {
    paramz <- unParameteriseTBATS(starting.params$vect, starting.params$control)
    lambda <- paramz$lambda
    alpha <- paramz$alpha
    beta.v <- paramz$beta
    if (!is.null(beta.v)) {
      adj.beta <- 1
    } else {
      adj.beta <- 0
    }
    b <- 0
    small.phi <- paramz$small.phi
    gamma.one.v <- paramz$gamma.one.v
    gamma.two.v <- paramz$gamma.two.v
    if (!is.null(seasonal.periods)) {
      s.vector <- numeric(2 * sum(k.vector))
    } else {
      s.vector <- NULL
    }
    # ar.coefs <- paramz$ar.coefs
    # ma.coefs <- paramz$ma.coefs
    ## Check for the existence of ARMA() coefficients
    if (!is.null(ar.coefs)) {
      p <- length(ar.coefs)
    } else {
      p <- 0
    }
    if (!is.null(ma.coefs)) {
      q <- length(ma.coefs)
    } else {
      q <- 0
    }
  }
  if (is.null(x.nought)) {
    # Start with the seed states equal to zero
    if (!is.null(ar.coefs)) {
      d.vector <- numeric(length(ar.coefs))
    } else {
      d.vector <- NULL
    }
    if (!is.null(ma.coefs)) {
      epsilon.vector <- numeric(length(ma.coefs))
    } else {
      epsilon.vector <- NULL
    }
    x.nought <- makeXMatrix(
      l = 0,
      b = b,
      s.vector = s.vector,
      d.vector = d.vector,
      epsilon.vector = epsilon.vector
    )$x
  }

  # Make the parameter vector  parameterise
  param.vector <- parameterise(
    alpha = alpha,
    beta.v = beta.v,
    small.phi = small.phi,
    gamma.v = cbind(gamma.one.v, gamma.two.v),
    lambda = lambda,
    ar.coefs = ar.coefs,
    ma.coefs = ma.coefs
  )
  par.scale <- makeParscale(param.vector$control)
  if (!is.null(seasonal.periods)) {
    tau <- as.integer(2 * sum(k.vector))
  } else {
    tau <- 0L
  }

  w <- makeTBATSWMatrix(
    smallPhi = small.phi,
    kVector = k.vector,
    arCoefs = ar.coefs,
    maCoefs = ma.coefs,
    tau = tau
  )

  if (!is.null(seasonal.periods)) {
    gamma.bold <- matrix(0, nrow = 1, ncol = (2 * sum(k.vector)))
    updateTBATSGammaBold(
      gammaBold = gamma.bold,
      kVector = k.vector,
      gammaOne = gamma.one.v,
      gammaTwo = gamma.two.v
    )
  } else {
    gamma.bold <- NULL
  }
  g <- matrix(0, nrow = ((2 * sum(k.vector)) + 1 + adj.beta + p + q), ncol = 1)
  if (p != 0) {
    g[(1 + adj.beta + tau + 1), 1] <- 1
  }
  if (q != 0) {
    g[(1 + adj.beta + tau + p + 1), 1] <- 1
  }
  updateTBATSGMatrix(
    g = g,
    gammaBold = gamma.bold,
    alpha = alpha,
    beta = beta.v
  )
  F <- makeTBATSFMatrix(
    alpha = alpha,
    beta = beta.v,
    small.phi = small.phi,
    seasonal.periods = seasonal.periods,
    k.vector = k.vector,
    gamma.bold.matrix = gamma.bold,
    ar.coefs = ar.coefs,
    ma.coefs = ma.coefs
  )
  D <- F - g %*% w$w.transpose

  ####
  # Set up environment
  opt.env <- new.env(parent = emptyenv())
  opt.env$F <- F
  opt.env$w.transpose <- w$w.transpose
  opt.env$g <- g
  opt.env$gamma.bold <- gamma.bold
  opt.env$k.vector <- k.vector
  opt.env$y <- matrix(y, nrow = 1, ncol = length(y))
  opt.env$y.hat <- matrix(0, nrow = 1, ncol = length(y))
  opt.env$e <- matrix(0, nrow = 1, ncol = length(y))
  opt.env$x <- matrix(0, nrow = length(x.nought), ncol = length(y))

  ## Set up matrices to find the seed states
  if (use.box.cox) {
    y.transformed <- BoxCox(y, lambda = lambda)
    lambda <- attr(y.transformed, "lambda")
    calcTBATSFaster(
      y = matrix(y.transformed, nrow = 1, ncol = length(y.transformed)),
      yHat = opt.env$y.hat,
      wTranspose = opt.env$w.transpose,
      F = opt.env$F,
      x = opt.env$x,
      g = opt.env$g,
      e = opt.env$e,
      xNought = x.nought
    )
    y.tilda <- opt.env$e
  } else {
    calcTBATSFaster(
      y = opt.env$y,
      yHat = opt.env$y.hat,
      wTranspose = opt.env$w.transpose,
      F = opt.env$F,
      x = opt.env$x,
      g = opt.env$g,
      e = opt.env$e,
      xNought = x.nought
    )
    y.tilda <- opt.env$e
  }
  w.tilda.transpose <- matrix(0, nrow = length(y), ncol = ncol(w$w.transpose))
  w.tilda.transpose[1, ] <- w$w.transpose
  w.tilda.transpose <- calcWTilda(
    wTildaTranspose = w.tilda.transpose,
    D = D
  )
  # Remove the AR() and MA() bits if they exist
  if ((p != 0) || (q != 0)) {
    end.cut <- ncol(w.tilda.transpose)
    start.cut <- end.cut - (p + q) + 1
    w.tilda.transpose <- w.tilda.transpose[, -c(start.cut:end.cut)]
  }
  x.nought <- lm(t(y.tilda) ~ w.tilda.transpose - 1)$coefficients
  x.nought <- matrix(x.nought, nrow = length(x.nought), ncol = 1)
  ## Replace the AR() and MA() bits if they exist
  if ((p != 0) || (q != 0)) {
    arma.seed.states <- numeric((p + q))
    arma.seed.states <- matrix(
      arma.seed.states,
      nrow = length(arma.seed.states),
      ncol = 1
    )
    x.nought <- rbind(x.nought, arma.seed.states)
  }

  ## Optimisation
  if (use.box.cox) {
    # Un-transform the seed states
    opt.env$x.nought.untransformed <- InvBoxCox(x.nought, lambda = lambda)
    # Optimise the likelihood function
    optim.like <- optim(
      par = param.vector$vect,
      fn = calcLikelihoodTBATS,
      method = "Nelder-Mead",
      opt.env = opt.env,
      use.beta = use.beta,
      use.small.phi = use.damping,
      seasonal.periods = seasonal.periods,
      param.control = param.vector$control,
      p = p,
      q = q,
      tau = tau,
      bc.lower = bc.lower,
      bc.upper = bc.upper,
      control = list(
        maxit = (100 * length(param.vector$vect)^2),
        parscale = par.scale
      )
    )
    # Get the parameters out of the param.vector
    paramz <- unParameteriseTBATS(optim.like$par, param.vector$control)
    lambda <- paramz$lambda
    alpha <- paramz$alpha
    beta.v <- paramz$beta
    small.phi <- paramz$small.phi
    gamma.one.v <- paramz$gamma.one.v
    gamma.two.v <- paramz$gamma.two.v
    if (!is.null(paramz$ar.coefs)) {
      p <- length(paramz$ar.coefs)
      ar.coefs <- matrix(paramz$ar.coefs, nrow = 1, ncol = p)
    } else {
      ar.coefs <- NULL
      p <- 0
    }
    if (!is.null(paramz$ma.coefs)) {
      q <- length(paramz$ma.coefs)
      ma.coefs <- matrix(paramz$ma.coefs, nrow = 1, ncol = q)
    } else {
      ma.coefs <- NULL
      q <- 0
    }
    # Transform the seed states
    x.nought <- BoxCox(opt.env$x.nought.untransformed, lambda = lambda)
    lambda <- attr(x.nought, "lambda")

    ## Calculate the variance:
    # 1. Re-set up the matrices
    w <- makeTBATSWMatrix(
      smallPhi = small.phi,
      kVector = k.vector,
      arCoefs = ar.coefs,
      maCoefs = ma.coefs,
      tau = tau
    )
    if (!is.null(gamma.bold)) {
      updateTBATSGammaBold(
        gammaBold = gamma.bold,
        kVector = k.vector,
        gammaOne = gamma.one.v,
        gammaTwo = gamma.two.v
      )
    }
    updateTBATSGMatrix(
      g = g,
      gammaBold = gamma.bold,
      alpha = alpha,
      beta = beta.v
    )
    updateFMatrix(
      F,
      small.phi,
      alpha,
      beta.v,
      gamma.bold,
      ar.coefs,
      ma.coefs,
      tau
    )

    # 2. Calculate!
    y.transformed <- BoxCox(y, lambda = lambda)
    lambda <- attr(y.transformed, "lambda")
    fitted.values.and.errors <- calcModel(y.transformed, x.nought, F, g, w)
    e <- fitted.values.and.errors$e
    variance <- sum((e * e)) / length(y)
    fitted.values <- InvBoxCox(
      fitted.values.and.errors$y.hat,
      lambda = lambda,
      biasadj,
      variance
    )
    attr(lambda, "biasadj") <- biasadj
  } else {
    # else if we are not using the Box-Cox transformation
    # Optimise the likelihood function
    if (length(param.vector$vect) > 1) {
      optim.like <- optim(
        par = param.vector$vect,
        fn = calcLikelihoodNOTransformedTBATS,
        method = "Nelder-Mead",
        opt.env = opt.env,
        x.nought = x.nought,
        use.beta = use.beta,
        use.small.phi = use.damping,
        seasonal.periods = seasonal.periods,
        param.control = param.vector$control,
        p = p,
        q = q,
        tau = tau,
        control = list(
          maxit = (100 * length(param.vector$vect)^2),
          parscale = par.scale
        )
      )
    } else {
      optim.like <- optim(
        par = param.vector$vect,
        fn = calcLikelihoodNOTransformedTBATS,
        method = "BFGS",
        opt.env = opt.env,
        x.nought = x.nought,
        use.beta = use.beta,
        use.small.phi = use.damping,
        seasonal.periods = seasonal.periods,
        param.control = param.vector$control,
        p = p,
        q = q,
        tau = tau,
        control = list(parscale = par.scale)
      )
    }

    # Get the parameters out of the param.vector
    paramz <- unParameteriseTBATS(optim.like$par, param.vector$control)
    lambda <- paramz$lambda
    alpha <- paramz$alpha
    beta.v <- paramz$beta
    small.phi <- paramz$small.phi
    gamma.one.v <- paramz$gamma.one.v
    gamma.two.v <- paramz$gamma.two.v
    if (!is.null(paramz$ar.coefs)) {
      p <- length(paramz$ar.coefs)
      ar.coefs <- matrix(paramz$ar.coefs, nrow = 1, ncol = p)
    } else {
      ar.coefs <- NULL
      p <- 0
    }
    if (!is.null(paramz$ma.coefs)) {
      q <- length(paramz$ma.coefs)
      ma.coefs <- matrix(paramz$ma.coefs, nrow = 1, ncol = q)
    } else {
      ma.coefs <- NULL
      q <- 0
    }

    ## Calculate the variance:
    # 1. Re-set up the matrices
    w <- makeTBATSWMatrix(
      smallPhi = small.phi,
      kVector = k.vector,
      arCoefs = ar.coefs,
      maCoefs = ma.coefs,
      tau = tau
    )
    if (!is.null(gamma.bold)) {
      updateTBATSGammaBold(
        gammaBold = gamma.bold,
        kVector = k.vector,
        gammaOne = gamma.one.v,
        gammaTwo = gamma.two.v
      )
    }
    updateTBATSGMatrix(
      g = g,
      gammaBold = gamma.bold,
      alpha = alpha,
      beta = beta.v
    )
    updateFMatrix(
      F,
      small.phi,
      alpha,
      beta.v,
      gamma.bold,
      ar.coefs,
      ma.coefs,
      tau
    )
    # 2. Calculate!
    fitted.values.and.errors <- calcModel(y, x.nought, F, g, w)
    e <- fitted.values.and.errors$e
    fitted.values <- fitted.values.and.errors$y.hat
    variance <- sum((e * e)) / length(y)
  }
  # Get the likelihood
  likelihood <- optim.like$value
  # Calculate the AIC
  aic <- likelihood + 2 * (length(param.vector$vect) + nrow(x.nought))

  # Make a list object
  fits <- ts(c(fitted.values))
  e <- ts(c(e))
  tsp(fits) <- tsp(e) <- tsp(y)
  model.for.output <- list(
    lambda = lambda,
    alpha = alpha,
    beta = beta.v,
    damping.parameter = small.phi,
    gamma.one.values = gamma.one.v,
    gamma.two.values = gamma.two.v,
    ar.coefficients = ar.coefs,
    ma.coefficients = ma.coefs,
    likelihood = likelihood,
    optim.return.code = optim.like$convergence,
    variance = variance,
    AIC = aic,
    parameters = list(vect = optim.like$par, control = param.vector$control),
    seed.states = x.nought,
    fitted.values = fits,
    errors = e,
    x = fitted.values.and.errors$x,
    seasonal.periods = seasonal.periods,
    k.vector = k.vector,
    y = y,
    p = p,
    q = q
  )
  class(model.for.output) <- c("fc_model", "tbats", "bats")
  model.for.output
}


calcLikelihoodTBATS <- function(
  param.vector,
  opt.env,
  use.beta,
  use.small.phi,
  seasonal.periods,
  param.control,
  p = 0,
  q = 0,
  tau = 0,
  bc.lower = 0,
  bc.upper = 1
) {
  # param vector should be as follows: Box-Cox.parameter, alpha, beta, small.phi, gamma.vector, ar.coefs, ma.coefs
  # Put the components of the param.vector into meaningful individual variables
  paramz <- unParameteriseTBATS(param.vector, param.control)
  box.cox.parameter <- paramz$lambda
  alpha <- paramz$alpha
  beta.v <- paramz$beta
  small.phi <- paramz$small.phi
  gamma.one.v <- paramz$gamma.one.v
  gamma.two.v <- paramz$gamma.two.v
  ar.coefs <- paramz$ar.coefs
  ma.coefs <- paramz$ma.coefs
  if (!is.null(paramz$ar.coefs)) {
    p <- length(paramz$ar.coefs)
    ar.coefs <- matrix(paramz$ar.coefs, nrow = 1, ncol = p)
  } else {
    ar.coefs <- NULL
    p <- 0
  }
  if (!is.null(paramz$ma.coefs)) {
    q <- length(paramz$ma.coefs)
    ma.coefs <- matrix(paramz$ma.coefs, nrow = 1, ncol = q)
  } else {
    ma.coefs <- NULL
    q <- 0
  }
  x.nought <- BoxCox(opt.env$x.nought.untransformed, lambda = box.cox.parameter)

  updateWtransposeMatrix(
    wTranspose = opt.env$w.transpose,
    smallPhi = small.phi,
    tau = as.integer(tau),
    arCoefs = ar.coefs,
    maCoefs = ma.coefs,
    p = as.integer(p),
    q = as.integer(q)
  )

  if (!is.null(opt.env$gamma.bold)) {
    updateTBATSGammaBold(
      gammaBold = opt.env$gamma.bold,
      kVector = opt.env$k.vector,
      gammaOne = gamma.one.v,
      gammaTwo = gamma.two.v
    )
  }
  updateTBATSGMatrix(
    g = opt.env$g,
    gammaBold = opt.env$gamma.bold,
    alpha = alpha,
    beta = beta.v
  )

  updateFMatrix(
    opt.env$F,
    small.phi,
    alpha,
    beta.v,
    opt.env$gamma.bold,
    ar.coefs,
    ma.coefs,
    tau
  )

  mat.transformed.y <- BoxCox(opt.env$y, box.cox.parameter)
  n <- ncol(opt.env$y)

  calcTBATSFaster(
    y = mat.transformed.y,
    yHat = opt.env$y.hat,
    wTranspose = opt.env$w.transpose,
    F = opt.env$F,
    x = opt.env$x,
    g = opt.env$g,
    e = opt.env$e,
    xNought = x.nought
  )

  ##
  ####
  ####################################################################

  log.likelihood <- n *
    log(sum(opt.env$e^2)) -
    2 * (box.cox.parameter - 1) * sum(log(opt.env$y))
  if (is.na(log.likelihood)) {
    # Not sure why this would occur
    return(Inf)
  }

  opt.env$D <- opt.env$F - opt.env$g %*% opt.env$w.transpose
  if (
    checkAdmissibility(
      opt.env,
      box.cox = box.cox.parameter,
      small.phi = small.phi,
      ar.coefs = ar.coefs,
      ma.coefs = ma.coefs,
      tau = sum(seasonal.periods),
      bc.lower = bc.lower,
      bc.upper = bc.upper
    )
  ) {
    return(log.likelihood)
  } else {
    return(Inf)
  }
}

calcLikelihoodNOTransformedTBATS <- function(
  param.vector,
  opt.env,
  x.nought,
  use.beta,
  use.small.phi,
  seasonal.periods,
  param.control,
  p = 0,
  q = 0,
  tau = 0
) {
  # The likelihood function without the Box-Cox Transformation
  # param vector should be as follows: alpha, beta, small.phi, gamma.vector, ar.coefs, ma.coefs
  # Put the components of the param.vector into meaningful individual variables
  paramz <- unParameteriseTBATS(param.vector, param.control)
  box.cox.parameter <- paramz$lambda
  alpha <- paramz$alpha
  beta.v <- paramz$beta
  small.phi <- paramz$small.phi
  gamma.one.v <- paramz$gamma.one.v
  gamma.two.v <- paramz$gamma.two.v

  if (!is.null(paramz$ar.coefs)) {
    p <- length(paramz$ar.coefs)
    ar.coefs <- matrix(paramz$ar.coefs, nrow = 1, ncol = p)
  } else {
    ar.coefs <- NULL
    p <- 0
  }

  if (!is.null(paramz$ma.coefs)) {
    q <- length(paramz$ma.coefs)
    ma.coefs <- matrix(paramz$ma.coefs, nrow = 1, ncol = q)
  } else {
    ma.coefs <- NULL
    q <- 0
  }

  updateWtransposeMatrix(
    wTranspose = opt.env$w.transpose,
    smallPhi = small.phi,
    tau = as.integer(tau),
    arCoefs = ar.coefs,
    maCoefs = ma.coefs,
    p = as.integer(p),
    q = as.integer(q)
  )

  if (!is.null(opt.env$gamma.bold)) {
    updateTBATSGammaBold(
      gammaBold = opt.env$gamma.bold,
      kVector = opt.env$k.vector,
      gammaOne = gamma.one.v,
      gammaTwo = gamma.two.v
    )
  }

  updateTBATSGMatrix(
    g = opt.env$g,
    gammaBold = opt.env$gamma.bold,
    alpha = alpha,
    beta = beta.v
  )

  updateFMatrix(
    opt.env$F,
    small.phi,
    alpha,
    beta.v,
    opt.env$gamma.bold,
    ar.coefs,
    ma.coefs,
    tau
  )

  n <- ncol(opt.env$y)

  calcTBATSFaster(
    y = opt.env$y,
    yHat = opt.env$y.hat,
    wTranspose = opt.env$w.transpose,
    F = opt.env$F,
    x = opt.env$x,
    g = opt.env$g,
    e = opt.env$e,
    xNought = x.nought
  )
  ##
  ####
  ####################################################################

  log.likelihood <- n * log(sum(opt.env$e * opt.env$e))
  if (is.na(log.likelihood)) {
    # Not sure why this would occur
    return(Inf)
  }

  opt.env$D <- opt.env$F - opt.env$g %*% opt.env$w.transpose

  if (
    checkAdmissibility(
      opt.env = opt.env,
      box.cox = NULL,
      small.phi = small.phi,
      ar.coefs = ar.coefs,
      ma.coefs = ma.coefs,
      tau = tau
    )
  ) {
    return(log.likelihood)
  } else {
    return(Inf)
  }
}