Irregular timestamps

TimeGPT can handle data with irregular timestamps. Simply specify the freq parameter with the right frequency of your data.

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'
)

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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 data
# Dates for the weekends are missing
df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/openbb/pltr.csv')

# Forecast
# We use B for the freq, as only business days are represented in the dataset
forecast_df = nixtla_client.forecast(
    df=df, 
    h=14, 
    freq='B',
    time_col='date', 
    target_col='Close',
)

INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
WARNING:nixtla.nixtla_client:You did not provide X_df. Exogenous variables in df are ignored. To surpress this warning, please add X_df with exogenous variables: Open, High, Low, Adj Close, Volume, Dividends, Stock Splits
WARNING:nixtla.nixtla_client:The specified horizon "h" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
INFO:nixtla.nixtla_client:Restricting input...
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...

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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 handling datasets with irregular timesteps, check out our tutorial.