Generate 'TimeGPT' forecast for the in-sample period (historical period).

Usage

nixtla_client_historic(
  df,
  freq = NULL,
  id_col = NULL,
  time_col = "ds",
  target_col = "y",
  level = NULL,
  quantiles = NULL,
  finetune_steps = 0,
  finetune_loss = "default",
  clean_ex_first = TRUE
)

Arguments

df: A tsibble or a data frame with time series data.
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.
level: The confidence levels (0-100) for the prediction intervals.
quantiles: Quantiles to forecast. Should be between 0 and 1.
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'.

Value

'TimeGPT''s forecast for the in-sample period.

Examples

if (FALSE) {
  nixtlar::nixtla_set_api_key("YOUR_API_KEY")
  df <- nixtlar::electricity
  fcst <- nixtlar::nixtla_client_historic(df, id_col="unique_id", level=c(80,95))
}