R/fitTBATS.R

In forecast: Forecasting Functions for Time Series and Linear Models

fitPreviousTBATSModel <- function(y, model, biasadj=FALSE) {
  seasonal.periods <- model$seasonal.periods
  if (is.null(seasonal.periods) == FALSE) {
    seasonal.periods <- sort(seasonal.periods)
  }
  # Get the parameters out of the param.vector
  paramz <- unParameteriseTBATS(model$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 <- as.integer(0)
    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) == FALSE) {
    y.touse <- BoxCox(y, lambda = lambda)
    lambda <- attr(y.touse, "lambda")
  }

  ## Calculate the variance:
  # 1. Re-set up the matrices
  w <- .Call("makeTBATSWMatrix", smallPhi_s = small.phi, kVector_s = model$k.vector, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, tau_s = tau, PACKAGE = "forecast")
  if (!is.null(gamma.bold)) {
    .Call("updateTBATSGammaBold", gammaBold_s = gamma.bold, kVector_s = model$k.vector, gammaOne_s = gamma.one.v, gammaTwo_s = gamma.two.v, PACKAGE = "forecast")
  }
  .Call("updateTBATSGMatrix", g_s = g, gammaBold_s = gamma.bold, alpha_s = alpha, beta_s = beta.v, PACKAGE = "forecast")
  F <- makeTBATSFMatrix(alpha = alpha, beta = beta.v, small.phi = small.phi, seasonal.periods = seasonal.periods, k.vector = model$k.vector, gamma.bold.matrix = gamma.bold, ar.coefs = ar.coefs, ma.coefs = ma.coefs)
  .Call("updateFMatrix", F, small.phi, alpha, beta.v, gamma.bold, ar.coefs, ma.coefs, tau, PACKAGE = "forecast")
  # 2. Calculate!
  fitted.values.and.errors <- calcModel(y.touse, model$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
  return(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 <- as.integer(0)
  }

  w <- .Call("makeTBATSWMatrix", smallPhi_s = small.phi, kVector_s = k.vector, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, tau_s = tau, PACKAGE = "forecast")

  if (!is.null(seasonal.periods)) {
    gamma.bold <- matrix(0, nrow = 1, ncol = (2 * sum(k.vector)))
    .Call("updateTBATSGammaBold", gammaBold_s = gamma.bold, kVector_s = k.vector, gammaOne_s = gamma.one.v, gammaTwo_s = gamma.two.v, PACKAGE = "forecast")
  } 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
  }
  .Call("updateTBATSGMatrix", g_s = g, gammaBold_s = gamma.bold, alpha_s = alpha, beta_s = beta.v, PACKAGE = "forecast")
  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()
  assign("F", F, envir = opt.env)
  assign("w.transpose", w$w.transpose, envir = opt.env)
  assign("g", g, envir = opt.env)
  assign("gamma.bold", gamma.bold, envir = opt.env)
  assign("k.vector", k.vector, envir = opt.env)
  assign("y", matrix(y, nrow = 1, ncol = length(y)), envir = opt.env)
  assign("y.hat", matrix(0, nrow = 1, ncol = length(y)), envir = opt.env)
  assign("e", matrix(0, nrow = 1, ncol = length(y)), envir = opt.env)
  assign("x", matrix(0, nrow = length(x.nought), ncol = length(y)), envir = opt.env)

  ## Set up matrices to find the seed states
  if (use.box.cox) {
    y.transformed <- BoxCox(y, lambda = lambda)
    lambda <- attr(y.transformed, "lambda")
    .Call("calcTBATSFaster", ys = matrix(y.transformed, nrow = 1, ncol = length(y.transformed)), yHats = opt.env$y.hat, wTransposes = opt.env$w.transpose, Fs = opt.env$F, xs = opt.env$x, gs = opt.env$g, es = opt.env$e, xNought_s = x.nought, PACKAGE = "forecast")
    y.tilda <- opt.env$e
  } else {
    .Call("calcTBATSFaster", ys = opt.env$y, yHats = opt.env$y.hat, wTransposes = opt.env$w.transpose, Fs = opt.env$F, xs = opt.env$x, gs = opt.env$g, es = opt.env$e, xNought_s = x.nought, PACKAGE = "forecast")
    y.tilda <- opt.env$e
  }
  w.tilda.transpose <- matrix(0, nrow = length(y), ncol = ncol(w$w.transpose))
  w.tilda.transpose[1, ] <- w$w.transpose
  w.tilda.transpose <- .Call("calcWTilda", wTildaTransposes = w.tilda.transpose, Ds = D, PACKAGE = "forecast")
  # Remove the AR() and MA() bits if they exist
  if ((p != 0) | (q != 0)) {
    end.cut <- ncol(w.tilda.transpose)
    start.cut <- end.cut - (p + q) + 1
    w.tilda.transpose <- w.tilda.transpose[, -c(start.cut:end.cut)]
  }
  x.nought <- lm(t(y.tilda) ~ w.tilda.transpose - 1)$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
    assign("x.nought.untransformed", InvBoxCox(x.nought, lambda = lambda), envir = opt.env)
    # 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 <- .Call("makeTBATSWMatrix", smallPhi_s = small.phi, kVector_s = k.vector, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, tau_s = tau, PACKAGE = "forecast")
    if (!is.null(gamma.bold)) {
      .Call("updateTBATSGammaBold", gammaBold_s = gamma.bold, kVector_s = k.vector, gammaOne_s = gamma.one.v, gammaTwo_s = gamma.two.v, PACKAGE = "forecast")
    }
    .Call("updateTBATSGMatrix", g_s = g, gammaBold_s = gamma.bold, alpha_s = alpha, beta_s = beta.v, PACKAGE = "forecast")
    .Call("updateFMatrix", F, small.phi, alpha, beta.v, gamma.bold, ar.coefs, ma.coefs, tau, PACKAGE = "forecast")

    # 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
    # e <- InvBoxCox(e, lambda=lambda)
    ee <- y - fitted.values
  } 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 <- .Call("makeTBATSWMatrix", smallPhi_s = small.phi, kVector_s = k.vector, arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, tau_s = tau, PACKAGE = "forecast")
    if (!is.null(gamma.bold)) {
      .Call("updateTBATSGammaBold", gammaBold_s = gamma.bold, kVector_s = k.vector, gammaOne_s = gamma.one.v, gammaTwo_s = gamma.two.v, PACKAGE = "forecast")
    }
    .Call("updateTBATSGMatrix", g_s = g, gammaBold_s = gamma.bold, alpha_s = alpha, beta_s = beta.v, PACKAGE = "forecast")
    .Call("updateFMatrix", F, small.phi, alpha, beta.v, gamma.bold, ar.coefs, ma.coefs, tau, PACKAGE = "forecast")
    # 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("tbats", "bats")
  return(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)
  lambda <- attr(x.nought, "lambda")

  .Call("updateWtransposeMatrix", wTranspose_s = opt.env$w.transpose, smallPhi_s = small.phi, tau_s = as.integer(tau), arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, p_s = as.integer(p), q_s = as.integer(q), PACKAGE = "forecast")

  if (!is.null(opt.env$gamma.bold)) {
    .Call("updateTBATSGammaBold", gammaBold_s = opt.env$gamma.bold, kVector_s = opt.env$k.vector, gammaOne_s = gamma.one.v, gammaTwo_s = gamma.two.v)
  }
  .Call("updateTBATSGMatrix", g_s = opt.env$g, gammaBold_s = opt.env$gamma.bold, alpha_s = alpha, beta_s = beta.v, PACKAGE = "forecast")

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

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

  .Call("calcTBATSFaster", ys = mat.transformed.y, yHats = opt.env$y.hat, wTransposes = opt.env$w.transpose, Fs = opt.env$F, xs = opt.env$x, gs = opt.env$g, es = opt.env$e, xNought_s = x.nought, PACKAGE = "forecast")

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

  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)
  }

  assign("D", (opt.env$F - opt.env$g %*% opt.env$w.transpose), envir = opt.env)
  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
  }

  .Call("updateWtransposeMatrix", wTranspose_s = opt.env$w.transpose, smallPhi_s = small.phi, tau_s = as.integer(tau), arCoefs_s = ar.coefs, maCoefs_s = ma.coefs, p_s = as.integer(p), q_s = as.integer(q), PACKAGE = "forecast")

  if (!is.null(opt.env$gamma.bold)) {
    .Call("updateTBATSGammaBold", gammaBold_s = opt.env$gamma.bold, kVector_s = opt.env$k.vector, gammaOne_s = gamma.one.v, gammaTwo_s = gamma.two.v)
  }

  .Call("updateTBATSGMatrix", g_s = opt.env$g, gammaBold_s = opt.env$gamma.bold, alpha_s = alpha, beta_s = beta.v, PACKAGE = "forecast")

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

  n <- ncol(opt.env$y)

  .Call("calcTBATSFaster", ys = opt.env$y, yHats = opt.env$y.hat, wTransposes = opt.env$w.transpose, Fs = opt.env$F, xs = opt.env$x, gs = opt.env$g, es = opt.env$e, xNought_s = x.nought, PACKAGE = "forecast")
  ##
  ####
  ####################################################################

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

  assign("D", (opt.env$F - opt.env$g %*% opt.env$w.transpose), envir = opt.env)

  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)
  }
}