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name: time-series-forecasting description: "Use AutoGluon TimeSeriesPredictor and TimeSeriesDataFrame for forecasting workflows, covariates, probabilistic forecasts, metrics, model presets, evaluation, and time-series troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Time-Series Forecasting

Use this sub-skill when the task is about AutoGluon time-series forecasting with autogluon.timeseries, especially TimeSeriesDataFrame and TimeSeriesPredictor.

Route Here When

• The user needs multi-step forecasts, prediction intervals, quantiles, backtesting, leaderboard, or evaluate for time series.
• Data has item_id, timestamp, and a numeric target column, or must be converted into TimeSeriesDataFrame.
• The workflow needs known future covariates, past covariates, static features, frequency inference/resampling, or train/test horizon splits.
• The user asks about time-series model presets, Chronos, Chronos2, Toto, DeepAR, TemporalFusionTransformer, DirectTabular, RecursiveTabular, or statistical baselines.

Route Elsewhere

• General supervised classification/regression on rows: use ../tabular-ml/.
• Text, image, document, object detection, or semantic matching AutoMM workflows: use ../multimodal-automl/.
• Package installation, import/version checks, backend compatibility, or cross-subpackage save/load issues: use root ../../references/troubleshooting.md.

Read First

• references/data-formats.md for constructing TimeSeriesDataFrame, required columns, frequency, covariates, static features, and validation patterns.
• references/api-reference.md for TimeSeriesPredictor and TimeSeriesDataFrame method signatures and behavior.
• references/workflows.md for quick-start, covariate, backtesting, probabilistic forecast, save/load, and feature-importance workflows.
• references/model-and-metric-guide.md for model families, presets, metrics, quantiles, Chronos/Toto constraints, and offline-safe choices.
• references/troubleshooting.md for common forecasting failures and fixes.

Bundled Scripts

• scripts/validate_timeseries_frame.py: validates a tiny or user-provided CSV/Parquet file as a TimeSeriesDataFrame, checks schema, static features, frequency, series length, and known-covariate horizon coverage.
• scripts/timeseries_smoke.py: builds a tiny in-memory dataset and verifies TimeSeriesDataFrame; pass --fit to run a deliberately small local-model predictor fit.

Default Agent Strategy

1. Normalize the data into TimeSeriesDataFrame with item_id, timestamp, and target columns before discussing models.
2. Confirm prediction_length, frequency, target name, known future covariates, and whether static features must be carried into prediction.
3. For offline or quick validation, choose hyperparameters={"Naive": {}, "SeasonalNaive": {}, "Theta": {}} or presets="fast_training"; avoid Chronos/Toto unless model artifacts and required backends are available.
4. Use train_data, test_data = data.train_test_split(prediction_length) for evaluation and ensure every series is long enough for the requested horizon and validation windows.
5. Expect predictions as a TimeSeriesDataFrame with mean plus quantile columns such as 0.1, 0.5, and 0.9.