rlgt.control: Sets and initializes the control parameters
In Rlgt: Bayesian Exponential Smoothing Models with Trend Modifications

rlgt.control

R Documentation

Sets and initializes the control parameters

Description

This function initializes and sets the control parameters, i.e. hyperparameter values which control the prior distribution of the rlgtfit model. The purpose of this function is mainly to provide a default value for each of the hyperparameters. The function also accepts a customised set of values of the parameters as provided in the input of this function. This function is used in conjunction with the rlgt function.

Usage

rlgt.control(
  ADAPT_DELTA = 0.9,
  MAX_TREE_DEPTH = 12,
  NUM_OF_CHAINS = 4,
  NUM_OF_CORES = 4,
  ADD_JITTER = TRUE,
  CAUCHY_SD_DIV = 150,
  NUM_OF_ITER = 5000,
  MAX_NUM_OF_REPEATS = 2,
  MAX_RHAT_ALLOWED = 1.006,
  NUM_OF_SEASON_INIT_CYCLES = 3,
  MIN_NU = 2,
  MAX_NU = 20,
  MIN_POW_TREND = -0.5,
  MAX_POW_TREND = 1,
  POW_TREND_ALPHA = 1,
  POW_TREND_BETA = 1,
  POW_SEASON_ALPHA = 1,
  POW_SEASON_BETA = 1,
  MIN_SIGMA = 1e-10,
  MIN_VAL = 1e-30,
  MAX_VAL = 1e+38
)

Arguments

`ADAPT_DELTA`	Target Metropolis acceptance rate. See Stan manual. Suggested range is between (0.85-0.97).
`MAX_TREE_DEPTH`	NUTS maximum tree depth. See Stan manual for more details. Suggested range is between (10-15), defaut is 12.
`NUM_OF_CHAINS`	Number of MCMC chains. Suggested range is 3 to 4. Default is 4.
`NUM_OF_CORES`	Number of cores used for calculations. It can be smaller than NUM_OF_CHAINS, but for best computational speed, it should be equal to NUM_OF_CHAINS. Default is 4.
`ADD_JITTER`	Whether to add a very small amount (sd=min(y)*0.0001) of jitter to the input series. It is sometimes useful in cases of series with some perfectly flat sections. Default is TRUE.
`CAUCHY_SD_DIV`	Cauchy distribution is used for some parameters with non-obvious range. The error size hyperparameter of this distribution is calculated by dividing the max value of the time series by this constant. Suggested range is between (100,300). Default 150.
`NUM_OF_ITER`	Starting number of iterations for each chain. Suggested range is between (2000,10000). Default is 5000. Generally, the longer the series, the smaller is the value to reach convergence. Some models e.g. those with "innov" error size method are more difficult to fit and require more iterations.
`MAX_NUM_OF_REPEATS`	Maximum number of the sampling procedure repeats if the fit is unsatisfactorily, i.e. avgRHat>MAX_RHAT_ALLOWED. Each round will double the number of iterations which could potentially double the running time. Suggested range is between (2,4). Default is 2.
`MAX_RHAT_ALLOWED`	Maximum average value of Rhat's that suggests a good fit, i.e. the treshold below which the fit is considered as acceptable. Consult Stan's manual for more details on Rhat. Suggested range is between (1.005,1.02). Default is 1.006.
`NUM_OF_SEASON_INIT_CYCLES`	For seasonal models, number of seasonality periods used for establishing initial seasonality coefficients. Default is 3.
`MIN_NU`	Minimum degrees of freedom of the Student's distribution that is used in most models. Suggested range(1.2, 5). Default 2.
`MAX_NU`	Maximum degrees of freedom of the Student's distribution. Suggested range is between (15,30). Default 20.
`MIN_POW_TREND`	Minimum value of the global trend power coefficient. Suggested range is between (-1,0). Default -.5
`MAX_POW_TREND`	Maximum value of the global trend power coefficient. It should be 1 to allow the model to approach exponential growth when needed. Default is 1.
`POW_TREND_ALPHA`	Alpha parameter of Beta prior distribution. To make the forecast more upward curved, so to nudge it towards larger values, make the parameter larger. Suggested range is between (1,6) Default 1.
`POW_TREND_BETA`	Beta parameter of Beta prior distribution for the global trend power coefficient. 1 by default, see also above.
`POW_SEASON_ALPHA`	Alpha parameter of Beta distribution that is the prior of the power coefficient in the formula of the generalized seasonality in gSGT model. 1 by default, increasing it (say, to 3 or 5) will push the seasonality towards multiplicative behavior.
`POW_SEASON_BETA`	Beta parameter of Beta distribution that is the prior of the power coefficient in the formula of the generalized seasonality in gSGT model. 1 by default.
`MIN_SIGMA`	Minimum size of the fitted sigma, applied for numerical stability. Must be positive. 1e-10 by default.
`MIN_VAL`	Minimum value that forecast can take. Must be positive. 1e-30 by default.
`MAX_VAL`	Maximum value the forecast can take. 1e38 by default.