Perform cross validation with 'TimeGPT'.

Usage

nixtla_client_cross_validation(
  df,
  h = 8,
  freq = NULL,
  id_col = NULL,
  time_col = "ds",
  target_col = "y",
  X_df = NULL,
  level = NULL,
  quantiles = NULL,
  n_windows = 1,
  step_size = NULL,
  finetune_steps = 0,
  finetune_loss = "default",
  clean_ex_first = TRUE,
  model = "timegpt-1"
)

Arguments

df: A tsibble or a data frame with time series data.
h: Forecast horizon.
freq: Frequency of the data.
id_col: Column that identifies each series.
time_col: Column that identifies each timestep.
target_col: Column that contains the target variable.
X_df: A tsibble or a data frame with future exogenous variables.
level: The confidence levels (0-100) for the prediction intervals.
quantiles: Quantiles to forecast. Should be between 0 and 1.
n_windows: Number of windows to evaluate.
step_size: Step size between each cross validation window. If NULL, it will equal the forecast horizon (h).
finetune_steps: Number of steps used to finetune 'TimeGPT' in the new data.
finetune_loss: Loss function to use for finetuning. Options are: "default", "mae", "mse", "rmse", "mape", and "smape".
clean_ex_first: Clean exogenous signal before making the forecasts using 'TimeGPT'.
model: Model to use, either "timegpt-1" or "timegpt-1-long-horizon". Use "timegpt-1-long-horizon" if you want to forecast more than one seasonal period given the frequency of the data.

Value

A tsibble or a data frame with 'TimeGPT''s cross validation result.

Examples

if (FALSE) {
  nixtlar::nixtla_set_api_key("YOUR_API_KEY")
  df <- nixtlar::electricity
  fcst <- nixtlar::nixtla_client_cross_validation(df, h = 8, id_col = "unique_id", n_windows = 5)
}