Quickstart

To perform 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'
)

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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://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/peyton_manning.csv')

# Detect anomalies
anomalies_df = nixtla_client.detect_anomalies(
    df, 
    time_col='timestamp', 
    target_col='value', 
    freq='D',
)

# Plot anomalies
nixtla_client.plot(
    df, 
    anomalies_df,
    time_col='timestamp', 
    target_col='value'
)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...

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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 anomaly detection with TimeGPT, check out our tutorial.