Detect anomalies with 'TimeGPT'
nixtla_client_detect_anomalies.Rd
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.
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")
} # }