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/air_passengers.csv")

# Forecast
forecast_df = nixtla_client.forecast(
    df=df,
    h=12,
    time_col='timestamp',
    target_col="value"
)

# Plot predictions
nixtla_client.plot(
    df=df, 
    forecasts_df=forecast_df, 
    time_col='timestamp', 
    target_col='value'
)

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 use an Azure AI endpoint, set model="azureai"

nixtla_client.detect_anomalies(..., model="azureai")

For the public API, two models are supported: timegpt-1 and timegpt-1-long-horizon.

By default, timegpt-1 is used. See this tutorial for details on using timegpt-1-long-horizon.

Why TimeGPT?Add exogenous variables