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'
)
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...
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 handling datasets with irregular timesteps, check out our tutorial.
Updated 8 days ago