Install & Compatibility
Where this runs
tested against v? · pip install
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
build_error
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
TimeSeriesPredictor
✓ from autogluon.timeseries import TimeSeriesPredictor
TimeSeriesDataFrame
✓ from autogluon.timeseries import TimeSeriesDataFrame
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.
import pandas as pd
from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame
# Create dummy data for demonstration
data = {
"item_id": ["A", "A", "A", "B", "B", "B"],
"timestamp": pd.to_datetime(["2023-01-01", "2023-01-02", "2023-01-03",
"2023-01-01", "2023-01-02", "2023-01-03"]),
"target": [10, 12, 15, 20, 22, 25],
}
df = pd.DataFrame(data)
# Convert to TimeSeriesDataFrame format
train_data = TimeSeriesDataFrame(df)
# Initialize and train the predictor
predictor = TimeSeriesPredictor(
prediction_length=2, # Forecast 2 future time steps
target="target", # Column to forecast
eval_metric="MASE" # Evaluation metric
)
predictor.fit(train_data, presets="fast_training") # Use a fast preset for quick demo
# Make predictions
predictions = predictor.predict(train_data)
print(predictions.head())
Debug
Known issues
breakingPython 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.fixEnsure your Python environment meets the specific `requires_python` range for your AutoGluon version before upgrading or deploying.
affects: All versions, specifically 1.2.0+
gotchaModels 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.fixAlways load previously trained models using the *exact same version* of AutoGluon that they were originally trained on. Consider pinning AutoGluon versions in production environments.
affects: All versions
gotchaAutoGluon'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.fixStart 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.
affects: All versions
Errors
Common errors & fixes
ModuleNotFoundError: No module named 'autogluon.timeseries'
The 'autogluon.timeseries' module is not installed or not found in the Python environment.
fixInstall the module using pip: 'pip install autogluon.timeseries'.
ImportError: cannot import name 'TimeSeriesPredictor' from 'autogluon.timeseries'
The 'TimeSeriesPredictor' class is not available in the 'autogluon.timeseries' module, possibly due to an outdated version.
fixEnsure you have the latest version of AutoGluon installed: 'pip install --upgrade autogluon.timeseries'.
FileNotFoundError: No such file 'predictor.pkl'
The 'predictor.pkl' file is missing or not located in the specified directory when attempting to load a saved predictor.
fixVerify the correct path to the 'predictor.pkl' file and ensure it exists before loading.
ValueError: Target column 'target' not found in the training data set.
The specified target column is missing from the training dataset.
fixCheck the training dataset to ensure the target column is present and correctly named.
AssertionError: Predictor is already fit! To fit additional models create a new 'Predictor'.
An attempt was made to fit a predictor that has already been trained.
fixCreate a new instance of the predictor to fit additional models.
Upgrade
Version history
1.6.1latest on PyPI · released Aug 6, 2026
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Dependencies
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