AutoTS¶
autots is an automated time series forecasting package for Python.
Features:
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
Installation¶
pip install autots
Requirements: Python 3.6+, numpy, pandas, statsmodels, and scikit-learn.