View source: R/parsnip_sarima_reg.R
| Sarima_stan_fit_impl | R Documentation | 
Low-Level ARIMA function for translating modeltime to forecast
Sarima_stan_fit_impl( x, y, period = "auto", p = 0, d = 0, q = 0, P = 0, D = 0, Q = 0, chains = 4, iter = 2000, warmup = iter/2, adapt.delta = 0.9, tree.depth = 10, seed = NULL, ... )
| x | A dataframe of xreg (exogenous regressors) | 
| y | A numeric vector of values to fit | 
| period | A seasonal frequency. Uses "auto" by default. A character phrase of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided. | 
| p | The order of the non-seasonal auto-regressive (AR) terms. Often denoted "p" in pdq-notation. | 
| d | The order of integration for non-seasonal differencing. Often denoted "d" in pdq-notation. | 
| q | The order of the non-seasonal moving average (MA) terms. Often denoted "q" in pdq-notation. | 
| P | The order of the seasonal auto-regressive (SAR) terms. Often denoted "P" in PDQ-notation. | 
| D | The order of integration for seasonal differencing. Often denoted "D" in PDQ-notation. | 
| Q | The order of the seasonal moving average (SMA) terms. Often denoted "Q" in PDQ-notation. | 
| chains | An integer of the number of Markov Chains chains to be run, by default 4 chains are run. | 
| iter | An integer of total iterations per chain including the warm-up, by default the number of iterations are 2000. | 
| warmup | A positive integer specifying number of warm-up (aka burn-in) iterations. This also specifies the number of iterations used for step-size adaptation, so warm-up samples should not be used for inference. The number of warmup should not be larger than iter and the default is iter/2. | 
| adapt.delta | An optional real value between 0 and 1, the thin of the jumps in a HMC method. By default is 0.9 | 
| tree.depth | An integer of the maximum depth of the trees evaluated during each iteration. By default is 10. | 
| seed | An integer with the seed for using when predicting with the model. | 
| ... | Additional arguments passed to  | 
A modeltime model
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