To detect anomalies with exogenous variables, load a dataset with the exogenous features as columns and use the same detect_anomalies method. 
import pandas as pd
from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key = 'my_api_key_provided_by_nixtla'
)
👍 Use an Azure AI endpoint To use an Azure AI endpoint, set the base_url argument: nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key") 
# Read the dataset
# The dataset has exogenous features in its columns
df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-with-ex-vars.csv')

# Detect anomalies
anomalies_df = nixtla_client.detect_anomalies(
    df=df,
    time_col='ds',
    target_col='y',
)

# Plot anomalies
nixtla_client.plot(df, anomalies_df)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Inferred freq: H
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Querying model metadata...
INFO:nixtla.nixtla_client:Using the following exogenous features: ['Exogenous1', 'Exogenous2', 'day_0', 'day_1', 'day_2', 'day_3', 'day_4', 'day_5', 'day_6']
INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...

# Plot weights of date features
nixtla_client.weights_x.plot.barh(x='features', y='weights')
📘 Available models in Azure AI If you use an Azure AI endpoint, set model="azureai" nixtla_client.detect_anomalies(..., model="azureai") For the public API, two models are supported: timegpt-1 and timegpt-1-long-horizon. By default, timegpt-1 is used. See this tutorial for details on using timegpt-1-long-horizon.
Read our detailed guide on anomaly detection for more information.