Predictions intervals
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
andtimegpt-1-long-horizon
.By default,
timegpt-1
is used. Please see this tutorial on how and when to usetimegpt-1-long-horizon
.
For more details on uncertainty quantification, read our tutorials on using quantile forecasts and prediction intervals.
Updated about 1 month ago