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",
  num_partitions = NULL
)

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.
num_partitions: A positive integer, "auto", or NULL specifying the number of partitions. When set to "auto", the number of partitions is equal to the number of available cores. When NULL, it defaults to a single partition.

Value

'TimeGPT”s forecast.

Examples

if (FALSE) { # \dontrun{
  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))
} # }