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If your dataset lacks exogenous variables, add date features to inform the model for historical anomaly detection. Use the date_features argument. Set it to True to extract all possible features, or pass a list of specific features to include. 
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 data
df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/peyton-manning.csv')

# Add date features for anomaly detection
# Here, we use date features at the month and year levels
anomalies_df_x = nixtla_client.detect_anomalies(
    df,
    freq='D', 
    date_features=['month', 'year'],
    date_features_to_one_hot=True,
    level=99.99,
)

# Plot anomalies
nixtla_client.plot(df, anomalies_df_x)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Querying model metadata...
INFO:nixtla.nixtla_client:Using the following exogenous features: ['month_1.0', 'month_2.0', 'month_3.0', 'month_4.0', 'month_5.0', 'month_6.0', 'month_7.0', 'month_8.0', 'month_9.0', 'month_10.0', 'month_11.0', 'month_12.0', 'year_2007.0', 'year_2008.0', 'year_2009.0', 'year_2010.0', 'year_2011.0', 'year_2012.0', 'year_2013.0', 'year_2014.0', 'year_2015.0', 'year_2016.0']
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.
For more details, check out our in-depth tutorial on anomaly detection.