Anomaly detection

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Import packages

First, we import the required packages for this tutorial and create an instance of NixtlaClient.

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")

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Load dataset

Now, let’s load the dataset for this tutorial. We use the Peyton Manning dataset which tracks the visits to the Wikipedia page of Peyton Mannig.

df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/peyton-manning.csv')
df.head()

nixtla_client.plot(df, max_insample_length=365)

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Anomaly detection

We now perform anomaly detection. By default, TimeGPT uses a 99% confidence interval. If a point falls outisde of that interval, it is considered to be an anomaly.

anomalies_df = nixtla_client.detect_anomalies(df, freq='D')
anomalies_df.head()

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

📘 Available models in Azure AI

If you are using an Azure AI endpoint, please be sure to set model="azureai":

nixtla_client.detect_anomalies(..., model="azureai")

For the public API, we support two models: timegpt-1 and timegpt-1-long-horizon.

By default, timegpt-1 is used. Please see this tutorial on how and when to use timegpt-1-long-horizon.

As you can see, 0 is assigned to “normal” values, as they fall inside the confidence interval. A label of 1 is then assigned to abnormal points.

We can also plot the anomalies using NixtlaClient.

nixtla_client.plot(df, anomalies_df)

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Anomaly detection with exogenous features

Previously, we performed anomaly detection without using any exogenous features. Now, it is possible to create features specifically for this scnenario to inform the model in its task of anomaly detection.

Here, we create date features that can be used by the model.

This is done using the date_features argument. We can set it to True and it will generate all possible features from the given dates and frequency of the data. Alternatively, we can specify a list of features that we want. In this case, we want only features at the month and year level.

anomalies_df_x = nixtla_client.detect_anomalies(
    df,
    freq='D', 
    date_features=['month', 'year'],
    date_features_to_one_hot=True,
)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
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...

Then, we can plot the weights of each feature to understand its impact on anomaly detection.

nixtla_client.weights_x.plot.barh(x='features', y='weights')

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Modifying the confidence intervals

We can tweak the confidence intervals using the level argument. This takes any values between 0 and 100, including decimal numbers.

Reducing the confidence interval resutls in more anomalies being detected, while increasing it will reduce the number of anomalies.

Here, for example, we reduce the interval to 70%, and we will notice more anomalies being plotted (red dots).

anomalies_df = nixtla_client.detect_anomalies(
    df, 
    freq='D',
    level=70
)

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

nixtla_client.plot(df, anomalies_df)