!pip install -Uqq nixtla

from nixtla.utils import in_colab

IN_COLAB = in_colab()

if not IN_COLAB:
    from nixtla.utils import colab_badge
    from dotenv import load_dotenv
To forecast with TimeGPT, call the forecast method. Pass your DataFrame and specify your target and time column names. Then plot the predictions using the plot method. You can read about data requierments here. 
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") 
if not IN_COLAB:
    nixtla_client = NixtlaClient()

# 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.