AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale.
There are dozens of forecasting models usable in the sklearn
style of .fit()
and .predict()
.
These includes naive, statistical, machine learning, and deep learning models.
Additionally, there are over 30 time series specific transforms usable in the sklearn
style of .fit()
, .transform()
and .inverse_transform()
.
All of these function directly on Pandas Dataframes, without the need for conversion to proprietary objects.
All models support forecasting multivariate (multiple time series) outputs and also support probabilistic (upper/lower bound) forecasts. Most models can readily scale to tens and even hundreds of thousands of input series. Many models also support passing in user-defined exogenous regressors.
These models are all designed for integration in an AutoML feature search which automatically finds the best models, preprocessing, and ensembling for a given dataset through genetic algorithms.
Horizontal and mosaic style ensembles are the flagship ensembling types, allowing each series to receive the most accurate possible models while still maintaining scalability.
A combination of metrics and cross-validation options, the ability to apply subsets and weighting, regressor generation tools, simulation forecasting mode, event risk forecasting, live datasets, template import and export, plotting, and a collection of data shaping parameters round out the available feature set.
- Installation
- Basic Use
- Tips for Speed and Large Data
- Extended Tutorial GitHub or Docs
- Production Example
pip install autots
This includes dependencies for basic models, but additonal packages are required for some models and methods.
Input data for AutoTS is expected to come in either a long or a wide format:
- The wide format is a
pandas.DataFrame
with apandas.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
, andvalue_col
. No parameters are needed for wide data.
Lower-level functions are only designed for wide
style 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="fast", # "superfast", "default", "fast_parallel"
transformer_list="fast", # "superfast",
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
- Use appropriate model lists, especially the predefined lists:
superfast
(simple naive models) andfast
(more complex but still faster models, optimized for many series)fast_parallel
(a combination offast
andparallel
) orparallel
, given many CPU cores are availablen_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
, passingweights
for series will weight subset selection towards higher priority series. - if limited by RAM, it can be distributed by running multiple instances of AutoTS on different batches of data, having first imported a template pretrained as a starting point for all.
- if using
- Set
model_interrupt=True
which passes over the current model when aKeyboardInterrupt
iecrtl+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. Useimport_results
to recover. - While Transformations are pretty fast, setting
transformer_max_depth
to a lower number (say, 2) will increase speed. Also utilizetransformer_list
== 'fast' or 'superfast'. - 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'
withmodel_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
andmodels_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
andaggfunc
but is probably best done before passing data into AutoTS.
- this can be done by adjusting
- It will be faster if NaN's are already filled. If a search for optimal NaN fill method is not required, then fill any NaN with a satisfactory method before passing to class.
- 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.