Detect anomalies with 'TimeGPT'

Usage

nixtla_client_detect_anomalies(
  df,
  freq = NULL,
  id_col = NULL,
  time_col = "ds",
  target_col = "y",
  level = c(99),
  clean_ex_first = TRUE,
  model = "timegpt-1"
)

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 level (0-100) for the prediction interval used in anomaly detection. Default is 99.
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 the anomalies detected in the historical period.

Examples

if (FALSE) {
  nixtlar::nixtla_set_api_key("YOUR_API_KEY")
  df <- nixtlar::electricity
  fcst <- nixtlar::nixtla_client_anomaly_detection(df, id_col="unique_id")
}