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",
  num_partitions = NULL
)

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.
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

A tsibble or a data frame with the anomalies detected in the historical period.

Examples

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