Time Series Forecasting#
AutoGluon can forecast the future values of multiple time series given the historical data and other related covariates.
A single call to AutoGluon TimeSeriesPredictor’s fit() method trains multiple models to generate accurate probabilistic forecasts,
and does not require you to manually deal with cumbersome issues like model selection and hyperparameter tuning.
Under the hood, AutoGluon combines various state of the art forecasting algorithms.
These include established statical methods like ETS and ARIMA from
StatsForecast and statsmodels libraries,
efficient tree-based forecasters like LightGBM based on AutoGluon-Tabular,
and flexible deep learning models like DeepAR and Temporal Fusion Transformer from GluonTS.
Check out the Quick Start Tutorial to learn how to make accurate forecasts in just 3 lines of code using AutoGluon.