​
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'
)

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

df.head()
timestampvalue
02007-12-109.590761
12007-12-118.519590
22007-12-128.183677
32007-12-138.072467
42007-12-147.893572
nixtla_client.plot(
    df,
    time_col='timestamp',
    target_col='value',
    max_insample_length=365
)

​
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, 
    time_col='timestamp', 
    target_col='value', 
    freq='D'
)

anomalies_df.head()

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...
timestampanomalyTimeGPT-lo-99TimeGPTTimeGPT-hi-99
02008-01-1006.9360098.2241949.512378
12008-01-1106.8633368.1515219.439705
22008-01-1206.8390648.1272499.415433
32008-01-1307.6290728.91725610.205441
42008-01-1407.7141119.00229510.290480

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,
    time_col='timestamp', 
    target_col='value'
)

​
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, time_col='timestamp', 
    target_col='value', 
    freq='D', 
    date_features=['month', 'year'],
)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...
INFO:nixtla.nixtla_client:Using the following exogenous variables: month_1, month_2, month_3, month_4, month_5, month_6, month_7, month_8, month_9, month_10, month_11, month_12, year_2007, year_2008, year_2009, year_2010, year_2011, year_2012, year_2013, year_2014, year_2015, year_2016

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')

​
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, 
    time_col='timestamp', 
    target_col='value', 
    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,
    time_col='timestamp', 
    target_col='value'
)

AzureAI (coming soon)Exogenous variables