TimeGEN-1 Quickstart (Azure)

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Step 1: Set up a TimeGEN-1 endpoint account and generate your API key on Azure

• Go to ml.azure.com
• Sign in or create an account at Microsoft
• Click on ‘Models’ in the sidebar
• Search for ‘TimeGEN’ in the model catalog
• Select TimeGEN-1

• Click ‘Deploy’ and this will create an Endpoint

• Go to ‘Endpoint’ in the sidebar and you will see your TimeGEN-1 endpoint there
• In that Endpoint are the base URL and API Key you will use

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Step 2: Install Nixtla

pip install nixtla

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Step 3: Import the Nixtla TimeGPT client

from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    base_url = "YOUR_BASE_URL",
    api_key = "YOUR_API_KEY"
)

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Step 4: Start making forecasts!

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')
df.head()

nixtla_client.plot(df, time_col='timestamp', target_col='value')

📘 Data Requirements

• Make sure the target variable column does not have missing or non-numeric values.
• Do not include gaps/jumps in the datestamps (for the given frequency) between the first and late datestamps. The forecast function will not impute missing dates.
• The format of the datestamp column should be readable by Pandas (see this link for more details).

For further details go to Data Requirements.

👍 Save figures made with TimeGEN The plot method automatically displays figures when in a notebook environment. To save figures locally, you can do: fig = nixtla_client.plot(df, time_col='timestamp', target_col='value') fig.savefig('plot.png', bbox_inches='tight')

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Make forecasts

• df: A pandas DataFrame containing the time series data.
• h: Horizons is the number of steps ahead to forecast.
• freq: The frequency of the time series in Pandas format. See pandas’ available frequencies. (If you don’t provide any frequency, the SDK will try to infer it)
• time_col: The column that identifies the datestamp.
• target_col: The variable to forecast.

timegen_fcst_df = nixtla_client.forecast(df=df, h=12, freq='MS', time_col='timestamp', target_col='value')
timegen_fcst_df.head()

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Restricting input...
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...

nixtla_client.plot(df, timegen_fcst_df, time_col='timestamp', target_col='value')