---

name: custom-data-and-prediction description: "Prepare custom time-series CSVs, validate feature/target/frequency choices, run the repository prediction path, and troubleshoot data-loader failures for Informer2020 custom data jobs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Custom Data and Prediction

Use this sub-skill for --data custom jobs that start from a custom time-series CSV and need a safe path to validation, prediction, or loader debugging.

Read First

• references/data-formats.md
• references/prediction-workflow.md
• references/troubleshooting.md
• ../../scripts/make_tiny_forecast_csv.py
• ../../scripts/check_forecast_csv.py
• ../../scripts/run_forecasting_smoke.py

Route Here When

• The CSV is user-provided and must be shaped for the custom data loader.
• You need to choose or validate --features (S, M, MS), --target, --cols, --freq, --embed, or --inverse.
• You need to understand how Dataset_Custom or Dataset_Pred will slice, scale, and reorder the data.
• You need the prediction output name or a minimal smoke path before larger runs.

Delegate Elsewhere

• ../training-and-evaluation/SKILL.md for benchmark preset choice, hyperparameter tuning, checkpoint strategy, and metrics interpretation.
• Other sub-skills for model architecture, attention internals, or non-forecasting tasks.

Standard Handoff

1. Confirm the CSV schema, target name, and feature mode.
2. Check that the split is long enough for the chosen seq_len and pred_len.
3. Validate the file with the shared CSV checker; use the tiny fixture or smoke helper when you need a minimal end-to-end proof.
4. Run prediction with --do_predict when you want results/<setting>/real_prediction.npy; keep checkpoint and metric decisions with the training-and-evaluation skill.

Safe Defaults

• Keep the target in --cols whenever you supply --cols.
• Match enc_in, dec_in, and c_out to the selected feature mode and channel count.
• Prefer canonical frequency codes when both training and prediction must use the same cadence.
• Treat missing dates, too-short splits, and loader exceptions as data issues first.