---

name: forecasting description: "Guide Auto-PyTorch time series forecasting workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Forecasting

Use this sub-skill for Auto-PyTorch time series forecasting. It covers sequence validation, horizon setup, known future features, forecasting-specific search and prediction, and the public forecasting task API.

When to use

Choose this route when the user asks to:

• forecast future values from one or more time series
• format sequences with start_times, freq, or series_idx
• provide known future features
• control the forecast horizon with n_prediction_steps
• inspect or customize forecasting-specific initialization and metrics
• validate uni-variant versus multi-variant forecasting data

What this route owns

• TimeSeriesForecastingTask
• TimeSeriesForecastingPipeline
• TimeSeriesForecastingInputValidator
• TimeSeriesFeatureValidator
• TimeSeriesTargetValidator
• TimeSeriesSequence
• TimeSeriesForecastingDataset
• forecasting search, fit, predict, and score flows
• forecasting metrics and sliding-window setup

Main workflow

1. Decide whether the input is uni-variant or multi-variant.
2. Build the sequence layout:
   • lists of series, or
   • DataFrames with series identifiers
3. Set start_times and freq when the series need explicit temporal context.
4. Provide known_future_features if the forecast uses exogenous features that are already known for the future.
5. Use search(...) for end-to-end forecasting AutoML or get_search_space(...) / fit_pipeline(...) for a single configuration.
6. Call predict(...) with either prepared TimeSeriesSequence objects or the raw future-features path plus past_targets.

Common decisions

Data layout

• Uni-variant tasks may omit X_train and validate only targets.
• Multi-variant tasks require feature data.
• When series_idx is used, the feature DataFrame must contain those identifier columns.
• The validator preserves per-series order and emits sequence-aware outputs.

Search control

• enable_traditional_pipeline is disabled by default for forecasting.
• suggested_init_models and custom_init_setting_path let you bias the starting models.
• search_space_updates is often used to adjust window_size, batch_size, or batches-per-epoch when the default window is not appropriate.

Metrics

Typical forecasting metrics include:

• mean_MASE_forecasting
• median_MASE_forecasting
• mean_MAE_forecasting
• mean_MAPE_forecasting
• mean_MSE_forecasting

What to read next

• references/workflows.md for end-to-end forecasting recipes and input-shape examples
• references/api-reference.md for the main class and method signatures
• references/troubleshooting.md for missing-dependency, shape, and sequence-identity failures
• scripts/forecasting_smoke.py for a tiny synthetic sequence validation check

Do not use this route for

• tabular classification or regression
• image classification proof-of-concept paths
• repo-maintenance or CI workflows

Route tabular questions to sub-skills/tabular-automl/ instead.