Generate 'TimeGPT' forecast
nixtla_client_forecast.Rd
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.
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))
} # }