autots 0.3.6

Automated Time Series Forecasting

AutoTS

Forecasting Model Selection for Multiple Time Series

AutoML for forecasting with open-source time series implementations.

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

Table of Contents

• Features
• Installation
• Basic Use
• Tips for Speed and Large Data
• Extended Tutorial GitHub or Docs
• Production Example

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 seasonity of the data
• 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

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 load: _hourly, _monthly, _weekly, _yearly, or _live_daily
from autots import AutoTS, load_daily

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

model = AutoTS(
    forecast_length=21,
    frequency='infer',
    prediction_interval=0.9,
    ensemble=None,
    model_list="default",
    transformer_list="fast",
    drop_most_recent=1,
    max_generations=4,
    num_validations=2,
    validation_method="backwards"
)
model = model.fit(
    df,
    date_col='datetime' if long else None,
    value_col='value' if long else None,
    id_col='series_id' if long else None,
)

prediction = model.predict()
# plot a sample
prediction.plot(model.df_wide_numeric,
                series=model.df_wide_numeric.columns[0],
                start_date="2019-01-01")
# Print the details of the best model
print(model)

# point forecasts dataframe
forecasts_df = prediction.forecast
# upper and lower forecasts
forecasts_up, forecasts_low = prediction.upper_forecast, prediction.lower_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 extended_tutorial.md for a more detailed guide to features!

Also take a look at the production_example.py

Tips for Speed and Large Data:

• Use appropriate model lists, especially the predefined lists:
  • superfast (simple naive models) and fast (more complex but still faster models)
  • fast_parallel (a combination of fast and parallel) or parallel, given many CPU cores are available
    • n_jobs usually gets pretty close with ='auto' but adjust as necessary for the environment
  • see a dict of predefined lists (some defined for internal use) with from autots.models.model_list import model_lists
• Use the subset parameter when there are many similar series, subset=100 will often generalize well for tens of thousands of similar series.
  • if using subset, passing weights for series will weight subset selection towards higher priority series.
  • if limited by RAM, it can be easily distributed by running multiple instances of AutoTS on different batches of data, having first imported a template pretrained as a starting point for all.
• Set model_interrupt=True which passes over the current model when a KeyboardInterrupt ie crtl+c is pressed (although if the interrupt falls between generations it will stop the entire training).
• Use the result_file method of .fit() which will save progress after each generation - helpful to save progress if a long training is being done. Use import_results to recover.
• While Transformations are pretty fast, setting transformer_max_depth to a lower number (say, 2) will increase speed. Also utilize transformer_list.
• Check out this example of using AutoTS with pandas UDF.
• Ensembles are obviously slower to predict because they run many models, 'distance' models 2x slower, and 'simple' models 3x-5x slower.
  • ensemble='horizontal-max' with model_list='no_shared_fast' can scale relatively well given many cpu cores because each model is only run on the series it is needed for.
• Reducing num_validations and models_to_validate will decrease runtime but may lead to poorer model selections.
• For datasets with many records, upsampling (for example, from daily to monthly frequency forecasts) can reduce training time if appropriate.
  • this can be done by adjusting frequency and aggfunc but is probably best done before passing data into AutoTS.

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.