We can generate prediction intervals using the level parameter in the forecast method. It takes any values between 0 and 100, including decimal numbers. 
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, remember to set also 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/air_passengers.csv")

# Forecast using a 80% confidence interval
forecast_df = nixtla_client.forecast(
    df=df,
    h=12,
    time_col='timestamp',
    target_col="value",
    level=[80]
)

# Plot predictions with intervals
nixtla_client.plot(
    df=df, 
    forecasts_df=forecast_df, 
    time_col='timestamp', 
    target_col='value',
    level=[80]
)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Inferred freq: MS
INFO:nixtla.nixtla_client:Restricting input...
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...
📘 Available models in Azure AI If you are using an Azure AI endpoint, please be sure to set model="azureai": nixtla_client.forecast(..., 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.
For more details on uncertainty quantification, read our tutorials on using quantile forecasts and prediction intervals.