Add confidence levels
Tweak the confidence level used for anomaly detection. By default, if a value falls outside the 99% confidence interval, it is labeled as an anomaly.
Modify this with the level
parameter, which accepts any value between 0 and 100, including decimals.
Increasing the level
results in fewer anomalies detected, while decreasing the level
increases the number of anomalies detected.
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 data
df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/peyton_manning.csv')
# Anomaly detection using a 70% confidence interval
anomalies_df = nixtla_client.detect_anomalies(
df,
time_col='timestamp',
target_col='value',
freq='D',
level=70
)
# 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...
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
.
For more information, read our detailed tutorial on anomaly detection.
Updated about 1 month ago