AutoGluon-TimeSeries is the latest addition to AutoGluon, which helps you easily build powerful time series forecasting models with as little as three lines of code.

Time series forecasting is a common task in a wide array of industries as well as scientific domains. Having access to reliable forecasts for supply, demand, or capacity is crucial to planning for businesses. However, time series forecasting is a difficult problem, especially when thousands of potentially related time series are available, such as sales in a large catalog in ecommerce, or capacity at hundreds of operational sites.

Simple statistical or judgement-based forecasting methods are often already strong baselines that are difficult to improve on with novel machine learning (ML) methods. Moreover, applications of recent advances in ML to forecasting are varied, with few methods such as DeepAR [1] or Temporal Fusion Transformers [2] emerging as popular choices. However, these methods are difficult to train, tune, and deploy in production, requiring expert knowledge of ML and time series analysis.

AutoML is a fast-growing topic within ML, focusing on automating common tasks in ML pipelines, including feature preprocessing, model selection, model tuning, ensembling, and deployment. AutoGluon-TimeSeries is the latest addition to AutoGluon, one of the leading open-source AutoML solutions, and builds on AutoGluon’s powerful framework for AutoML in forecasting tasks. AutoGluon-TimeSeries was designed to build powerful forecasting systems with as little as three lines of code, alleviating the challenges of feature preprocessing, model selection, model tuning, and ease of deployment.

With a simple call to AutoGluon-TimeSeries’s TimeSeriesPredictor, AutoGluon follows an intuitive order of priority in fitting models: starting from simple naive baselines and moving to powerful global neural network and boosted tree-based methods, all within the time budget specified by the user. When related time series (time-varying covariates or exogenous variables) or item metadata (static features) are available, AutoGluon-TimeSeries factors them into the forecast. The library also taps into Bayesian optimization for hyperparameter tuning, arriving to the best model configuration by tuning complex models. Finally, AutoGluon-TimeSeries combines the best of statistical and ML-based methods into a model ensemble optimized for the problem at hand.

In this post, we showcase AutoGluon-TimeSeries’s ease of use in quickly building a powerful forecaster.

## Get started with AutoGluon-TimeSeries

To start, you need to install AutoGluon, which is easily done with pip on a UNIX shell:

pip install "autogluon>=0.6"

AutoGluon-TimeSeries introduces the TimeSeriesDataFrame class for working with datasets that include multiple related time series (sometimes called a panel dataset). These data frames can be created from so-called long format data frames, which have time series IDs and timestamps arranged into rows. The following is one such data example, taken from the M4 competition [3]. Here, the item_id column specifies the unique identifier of a single time series, such as the product ID for daily sales data of multiple products. The target column is the value of interest that AutoGluon-TimeSeries will learn to forecast. weekend is an extra time-varying covariate we produced to mark if the observation was on the weekend or not.

We can easily produce a new TimeSeriesDataFrame from this dataset using the from_data_frame constructor. See the following Python code:

df = TimeSeriesDataFrame.from_data_frame(raw_data_frame)

Some time series data has non-time-varying features (static features or item metadata) that can be used in training a forecasting model. For example, the M4 dataset features a category variable for each time series. These can be added to the TimeSeriesDataFrame by setting the static_features variable with a new data frame.

Use the following code:

df.static_features = raw_static_features

## Train a TimeSeriesPredictor

Finally, we can call the TimeSeriesPredictor to fit a wide array of forecasting models to build an accurate forecasting system. See the following code:

predictor = TimeSeriesPredictor( prediction_length=7, eval_metric="MASE", known_covariates_names=["weekend"],
)

Here, we specify that the TimeSeriesPredictor should produce models to forecast the next seven time periods and judge the best models by using mean absolute scaled error (MASE). Moreover, we indicate that the time-varying covariate weekend is available in the dataset. We can now fit the predictor object on the TimeSeriesDataFrame produced earlier:

predictor.fit(df, presets="medium_quality", time_limit=1800)

Apart from providing the training data, we ask the predictor to use “medium_quality” presets. AutoGluon-TimeSeries comes with multiple presets to select subsets of models to consider and how much time to spend tuning them, managing the trade-off between training speed vs. accuracy. Apart from presets, more experienced users can use a hyperparameters argument to precisely specify component models and which hyperparameters to set on them. We also specify a time limit of 1,800 seconds, after which the predictor stops training.

Under the hood, AutoGluon-TimeSeries trains as many models as it can within the specified time frame, starting from naive but powerful baselines and working towards more complex forecasters based on boosted trees and neural network models. By calling predictor.leaderboard(), we can see a list of all models it has trained and the accuracy scores and training times for each. Note that every AutoGluon-TimeSeries model reports its errors in a “higher is better” format, which means most forecasting error measures are multiplied by -1 when reported. See the following example:

 model score_val pred_time_val fit_time_marginal fit_order
0 WeightedEnsemble -0.612510 15.406334 48.428711 8
1 AutoGluonTabular -0.654924 1.068694 104.208688 6
2 DeepAR -0.673366 6.731659 1065.956648 7
3 SeasonalNaive -1.035286 0.410615 0.000742 2
4 ETS -1.073640 5.832542 0.000584 3
5 Theta -1.107362 1.773439 0.000614 4
6 ARIMA -3.006273 2.483140 0.000625 5
7 Naive -3.427339 29.532215 0.000577 1

## Forecast with a TimeSeriesPredictor

Finally, we can use the predictor to predict all time series in a TimeSeriesDataFrame, 7 days into the future. Note that because we used time-varying covariates that are assumed to be known in the future, these should also be specified at prediction time. See the following code:

predictions = predictor.predict( df, known_covariates=future_known_covariates
)

By default, AutoGluon-TimeSeries provides both point forecasts and probabilistic (quantile) forecasts of the target value. Probabilistic forecasts are essential in many planning tasks, and they can be used to flexibly compute intervals, enabling downstream tasks such as inventory and capacity planning.

The following is a sample forecast plot demonstrating point forecasts and prediction intervals.

## Conclusion

AutoGluon-TimeSeries gives forecasters and data scientists a quick and easy way to build powerful forecasting models. In addition to some of the library’s commonly used features showcased in this post, AutoGluon-TimeSeries features a set of ways to configure forecasts for advanced users. Predictors are also easy to train, deploy, and serve at scale with Amazon SageMaker, using AutoGluon deep learning containers.

For more details on using AutoGluon, examples, tutorials, as well as other tasks AutoGluon tackles such as learning on tabular or multimodal data, visit AutoGluon. To get started using AutoGluon-TimeSeries, check out our quick start tutorial or our in-depth tutorial for a deeper look into all features the library offers. Follow AutoGluon on Twitter, and star us on GitHub to be informed of the latest updates.

For forecasting at scale with dedicated compute and workflows, enterprise-level support, forecast explainability and more, also check out Amazon Forecast.

## References

[1] Salinas, David, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. “DeepAR: Probabilistic forecasting with autoregressive recurrent networks.” International Journal of Forecasting 36. 3 (2020): 1181-1191.

[2] Lim, Bryan, Sercan O Arik, Nicolas Loeff, and Tomas Pfister. “Temporal Fusion Transformers for interpretable multi-horizon time series forecasting.” International Journal of Forecasting 37.4 (2021): 1748-1764.

[3] Makridakis, Spyros, Evangelos Spiliotis, and Vassilios Assimakopoulos. “The M4 Competition: 100,000 time series and 61 forecasting methods.” International Journal of Forecasting 36.1 (2020): 54-74.