Add exogenous variables
To detect anomalies with exogenous variables, load a dataset with the exogenous features as columns. Use the same detect_anomalies
method and plot the weights of each feature using weight_x.plot()
.
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
# The dataset has exogenous features in its columns
df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-with-ex-vars.csv')
# Detect anomalies
anomalies_df = nixtla_client.detect_anomalies(
df=df,
time_col='ds',
target_col='y'
)
# Plot weight of exgeonous features
nixtla_client.weights_x.plot.barh(x='features', y='weights')
INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Inferred freq: H
INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...
INFO:nixtla.nixtla_client:Using the following exogenous variables: Exogenous1, Exogenous2, day_0, day_1, day_2, day_3, day_4, day_5, day_6
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
andtimegpt-1-long-horizon
.By default,
timegpt-1
is used. See this tutorial for details on usingtimegpt-1-long-horizon
.
Read our detailed guide on anomaly detection for more information.
Updated about 1 month ago