AutoML for forecasting with open-source time series implementations.


  • Finds optimal time series forecasting model and data transformations by genetic programming optimization
  • Handles univariate and multivariate/parallel time series
  • Point and probabilistic upper/lower bound forecasts for all models
  • Over twenty available model classes, with tens of thousands of possible hyperparameter configurations
    • Includes naive, statistical, machine learning, and deep learning models
    • Multiprocessing for univariate models for scalability on multivariate datasets
    • Ability to add external regressors
  • Over thirty time series specific data transformations
    • Ability to handle messy data by learning optimal NaN imputation and outlier removal
  • Allows automatic ensembling of best models
    • 'horizontal' ensembling on multivariate series - learning the best model for each series
  • Multiple cross validation options
    • 'seasonal' validation allows forecasts to be optimized for the season of your forecast period
  • Subsetting and weighting to improve speed and relevance of search on large datasets
    • 'constraint' parameter can be used to assure forecasts don't drift beyond historic boundaries
  • Option to use one or a combination of metrics for model selection
  • Import and export of model templates for deployment and greater user customization


pip install autots

This includes dependencies for basic models, but additonal packages are required for some models and methods.

Basic Use

Input data is expected to come in either a long or a wide format:

  • The wide format is a pandas.DataFrame with a pandas.DatetimeIndex and each column a distinct series.
  • The long format has three columns:
    • Date (ideally already in pd.DateTime format)
    • Series ID. For a single time series, series_id can be = None.
    • Value
  • For long data, the column name for each of these is passed to .fit() as date_col, id_col, and value_col. No parameters are needed for wide data.
# also: _hourly, _daily, _weekly, or _yearly
from autots.datasets import load_monthly

# sample datasets can be used in either of the long or wide import shapes
long = True
df = load_monthly(long=long)

from autots import AutoTS

model = AutoTS(
model =
    date_col='datetime' if long else None,
    value_col='value' if long else None,
    id_col='series_id' if long else None,

# Print the details of the best model

prediction = model.predict()
# point forecasts dataframe
forecasts_df = prediction.forecast
# accuracy of all tried model results
model_results = model.results()
# and aggregated from cross validation
validation_results = model.results("validation")

The lower-level API, in particular the large section of time series transformers in the scikit-learn style, can also be utilized independently from the AutoML framework.

Check out for a more detailed guide to features!

How to Contribute:

  • Give feedback on where you find the documentation confusing
  • Use AutoTS and...
    • Report errors and request features by adding Issues on GitHub
    • Posting the top model templates for your data (to help improve the starting templates)
    • Feel free to recommend different search grid parameters for your favorite models
  • And, of course, contributing to the codebase directly on GitHub!

Also known as Project CATS (Catlin's Automated Time Series) hence the logo.