To perform historical anomaly detection, use the detect_anomalies method. Then, plot the anomalies using the plot 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
df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/peyton-manning.csv')

# Detect anomalies
anomalies_df = nixtla_client.detect_anomalies(df, freq='D')

# Plot anomalies
nixtla_client.plot(df, anomalies_df)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...
📘 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 an in-depth guide on historical anomaly detection with TimeGPT, check out our tutorial.