Model Selection for Multiple Time Series

Simple package for comparing and predicting with open-source time series implementations.

For other time series needs, check out the list here.

• Twenty available model classes, with tens of thousands of possible hyperparameter configurations
• Finds optimal time series models by genetic programming
• Handles univariate and multivariate/parallel time series
• Point and probabilistic forecasts
• Ability to handle messy data by learning optimal NaN imputation and outlier removal
• Ability to add external known-in-advance regressor
• Allows automatic ensembling of best models
• Multiple cross validation options
• Subsetting and weighting to improve search on many multivariate series
• Option to use one or a combination of metrics for model selection
• Ability to upsample data to a custom frequency
• Import and export of templates allowing greater user customization

pip install autots

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

Input data is expected to come in a 'long' format with three columns:

• Date (ideally already in pd.DateTime format)
• Value
• Series ID. For a single time series, series_id can be = None.

The column name for each of these is passed to .fit().

# also: _hourly, _daily, _weekly, or _yearly
from autots.datasets import load_monthly 
df_long = load_monthly()

from autots import AutoTS
model = AutoTS(forecast_length=3, frequency='infer',
               prediction_interval=0.9, ensemble=None,
			   model_list='superfast',
               max_generations=5, num_validations=2,
			   validation_method='even')
model = model.fit(df_long, date_col='datetime',
				  value_col='value', id_col='series_id')

# Print the name of the best model
print(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")

Check out extended_tutorial.md for a more detailed guide to features!

• 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.