{"id":4445,"library":"autogluon-timeseries","title":"AutoGluon-TimeSeries","description":"AutoGluon-TimeSeries is a module within the AutoGluon automated machine learning library, specifically designed for robust and accurate time series forecasting. It enables users to train state-of-the-art forecasting models with minimal code, leveraging various algorithms and ensemble methods. The library is actively maintained with frequent major and minor releases, providing continuous improvements in performance and features. The current version is 1.5.0.","status":"active","version":"1.5.0","language":"en","source_language":"en","source_url":"https://github.com/autogluon/autogluon/","tags":["machine-learning","timeseries","forecasting","automl","deep-learning"],"install":[{"cmd":"pip install autogluon.timeseries","lang":"bash","label":"Install AutoGluon TimeSeries"}],"dependencies":[],"imports":[{"symbol":"TimeSeriesPredictor","correct":"from autogluon.timeseries import TimeSeriesPredictor"},{"symbol":"TimeSeriesDataFrame","correct":"from autogluon.timeseries import TimeSeriesDataFrame"}],"quickstart":{"code":"import pandas as pd\nfrom autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame\n\n# Create dummy data for demonstration\ndata = {\n    \"item_id\": [\"A\", \"A\", \"A\", \"B\", \"B\", \"B\"],\n    \"timestamp\": pd.to_datetime([\"2023-01-01\", \"2023-01-02\", \"2023-01-03\",\n                                \"2023-01-01\", \"2023-01-02\", \"2023-01-03\"]),\n    \"target\": [10, 12, 15, 20, 22, 25],\n}\ndf = pd.DataFrame(data)\n\n# Convert to TimeSeriesDataFrame format\ntrain_data = TimeSeriesDataFrame(df)\n\n# Initialize and train the predictor\npredictor = TimeSeriesPredictor(\n    prediction_length=2, # Forecast 2 future time steps\n    target=\"target\",     # Column to forecast\n    eval_metric=\"MASE\"   # Evaluation metric\n)\npredictor.fit(train_data, presets=\"fast_training\") # Use a fast preset for quick demo\n\n# Make predictions\npredictions = predictor.predict(train_data)\nprint(predictions.head())","lang":"python","description":"This quickstart demonstrates how to prepare time series data using `TimeSeriesDataFrame`, initialize `TimeSeriesPredictor`, train a model with a specified preset, and generate forecasts for future time steps."},"warnings":[{"fix":"Ensure your Python environment meets the specific `requires_python` range for your AutoGluon version before upgrading or deploying.","message":"Python version support changes significantly across major/minor releases. AutoGluon 1.2.0 dropped support for Python 3.8 and added 3.12. The current version (1.5.0) requires Python >=3.10 and <3.14.","severity":"breaking","affected_versions":"All versions, specifically 1.2.0+"},{"fix":"Always load previously trained models using the *exact same version* of AutoGluon that they were originally trained on. Consider pinning AutoGluon versions in production environments.","message":"Models trained with one version of AutoGluon are often not compatible with other versions (especially across major/minor releases). Attempting to load models saved with a different AutoGluon version can lead to errors or unexpected behavior.","severity":"gotcha","affected_versions":"All versions"},{"fix":"Start with `presets='fast_training'` or `presets='medium_quality'` for initial experimentation. Monitor resource usage and adjust `hyperparameters` or `time_limit` for larger datasets or production deployments.","message":"AutoGluon's `fit` method, especially with default `presets` or large datasets, can be very resource-intensive (CPU, RAM, GPU if enabled). Users may experience long training times or out-of-memory errors.","severity":"gotcha","affected_versions":"All versions"}],"env_vars":null,"last_verified":"2026-04-12T00:00:00.000Z","next_check":"2026-07-11T00:00:00.000Z"}