Release of new forecasting methods. Among the updates, we've unveiled the
timegpt-1-long-horizon model, crafted specifically for long-term forecasts that span multiple seasonalities. To use it, simply specify the model in your methods like so:
from nixtlats import TimeGPT # Initialize the TimeGPT model timegpt = TimeGPT() # Generate forecasts using the long-horizon model fcst_df = timegpt.forecast(..., model='timegpt-1-long-horizon') # Perform cross-validation with the long-horizon model cv_df = timegpt.cross_validation(..., model='timegpt-1-long-horizon') # Detect anomalies with the long-horizon model anomalies_df = timegpt.detect_anomalies(..., model='timegpt-1-long-horizon')
timegpt-1 for the first version of
timegpt-1-long-horizon for long horizon tasks..
You can dive deeper into your forecasting pipelines with the new
cross_validation feature. This method enables you to validate forecasts across different windows efficiently:
# Set up cross-validation with a custom horizon, number of windows, and step size cv_df = timegpt.cross_validation(df, h=35, n_windows=5, step_size=5)
This will generate 5 distinct forecast sets, each with a horizon of 35, stepping through your data every 5 timestamps.
The new retry mechanism allows the making of more robust API calls (preventing them from crashing with large-scale tasks).
max_retries: Number of max retries for an API call.
retry_interval: Pause between retries.
max_wait_time: Total duration of retries.
timegpt = TimeGPT(max_retries=10, retry_interval=5, max_wait_time=360)
TimeGPT class now automatically infers your
os.environ.get('TIMEGPT_TOKEN'), streamlining your setup:
# No more manual token handling - TimeGPT has got you covered timegpt = TimeGPT()
For more information visit our FAQS section.
Questions? We've got answers! Our new FAQ section tackles the most common inquiries, from integrating exogenous variables to configuring authorization tokens and understanding long-horizon forecasts.
See full changelog here.
Updated about 1 month ago