Generate 'TimeGPT' forecast

Usage

nixtla_client_forecast(
  df,
  h = 8,
  freq = NULL,
  id_col = NULL,
  time_col = "ds",
  target_col = "y",
  X_df = NULL,
  level = NULL,
  quantiles = NULL,
  finetune_steps = 0,
  finetune_loss = "default",
  clean_ex_first = TRUE,
  add_history = FALSE,
  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.
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'.
add_history: Return fitted values of the model.
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

'TimeGPT''s forecast.

Examples

if (FALSE) {
  nixtlar::nixtla_set_api_key("YOUR_API_KEY")
  df <- nixtlar::electricity
  fcst <- nixtlar::nixtla_client_forecast(df, h=8, id_col="unique_id", level=c(80,95))
}