Bayesian ARIMA(p,d,q) time-series analysis — arima

Fit an ARIMA(p,d,q) model with a Bayesian MCMC analysis. Wraps the shared C++ ARIMAAnalysis.

Usage

arima_analysis(
  data,
  order_p = 1L,
  order_d = 0L,
  order_q = 1L,
  include_intercept = TRUE,
  training_time_steps = NULL,
  forecasting_time_steps = 0L,
  sampler = "DEMCz",
  iterations = 3000L,
  output_length = 10000L,
  credible_level = 0.9,
  seed = 12345L,
  thinning_interval = -1L
)

Arguments

data: numeric vector of the observed time series (in sequence order).
order_p: autoregressive order (default 1).
order_d: differencing order (default 0).
order_q: moving-average order (default 1).
include_intercept: logical; include an intercept term (default TRUE).
training_time_steps: number of leading steps used for calibration; when NULL the model default (max(30, floor(0.8 * n))) is used, which is invalid for short series – set it explicitly (e.g. 15) when n is small.
forecasting_time_steps: number of steps to forecast past the observed series (default 0).
sampler: MCMC sampler: "DEMCz" (default), "DEMCzs", "ARWMH", or "NUTS".
iterations: number of post-warmup MCMC iterations.
output_length: number of posterior samples used to build the credible band.
credible_level: credible-interval width (e.g. 0.90 for a 90% band).
seed: PRNG seed for the sampler (fixed for reproducibility).
thinning_interval: MCMC thinning interval; -1 (default) keeps the sampler's own default.

Value

A named list with the same shape as ar_analysis().

Examples

x <- c(10.2, 11.5, 9.8, 12.1, 13.4, 11.9, 10.6, 12.8, 14.0, 13.1, 11.7, 12.5, 13.9, 15.2,
       14.1, 12.9, 13.6, 15.0, 16.2, 14.8)
fit <- arima_analysis(x, order_p = 1, order_d = 1, order_q = 1, training_time_steps = 15,
                      forecasting_time_steps = 3, iterations = 100, output_length = 200,
                      seed = 12345, thinning_interval = 1)
fit$aic
#> [1] 61.73414