forecasting

TSLib Forecasting

Use this sub-skill for TSLib forecasting tasks: long-term forecasting on ETT/custom/ECL/Traffic/Weather/Exchange/ILI-style CSVs, short-term M4 forecasting, TimeXer/exogenous variable recipes, zero-shot forecasting with large time-series models, and forecasting metric/output interpretation.

Route Here

• Build or revise python run.py --task_name long_term_forecast ... commands.
• Configure --seq_len, --label_len, --pred_len, --features, --target, channel counts, and forecast model dimensions.
• Use TimeXer or features MS for exogenous/target-only forecast settings.
• Run or debug short_term_forecast with M4 seasonal patterns and SMAPE/OWA outputs.
• Understand zero-shot --task_name zero_shot_forecast and optional LTSM model dependencies.
• Interpret metrics.npy, pred.npy, true.npy, result text files, plot PDFs, and M4 forecast CSVs.

Reroute

• Data file validation, UEA/anomaly layouts, generic CLI outputs, or CPU/GPU preflight: use ../data-and-cli/SKILL.md.
• Imputation, anomaly detection, and classification: use ../imputation-anomaly-classification/SKILL.md.
• Adding models, Mamba/LTSM dependency installation, and augmentation flags: use ../foundation-models-and-customization/SKILL.md.

Start Fast

Render a small command rather than copying a benchmark script verbatim:

python scripts/build_tslib_command.py long-term \
  --model DLinear --root-path ./dataset/tiny-custom/ --data-path tiny.csv \
  --model-id tiny_custom --features M --channels 3 \
  --seq-len 8 --label-len 4 --pred-len 4 --cpu-smoke

Then run the printed command from the TSLib checkout. Use real benchmark scripts only after paths, GPU, dependencies, and expected cost are clear.

Forecasting Modes

• Long-term forecast: --task_name long_term_forecast; uses sliding windows and writes MAE/MSE/RMSE/MAPE/MSPE plus optional DTW.
• Short-term/M4 forecast: --task_name short_term_forecast --data m4; uses M4 seasonal patterns and losses such as SMAPE. Final averaged M4 metrics print after all six seasonal forecast CSVs exist.
• Exogenous/TimeXer forecast: still uses long_term_forecast; TimeXer recipes often set --model TimeXer and may use features MS when forecasting a target with exogenous inputs.
• Zero-shot forecast: --task_name zero_shot_forecast --is_training 0; model files such as Chronos, Moirai, TimesFM, TiRex, Sundial, and TimeMoE can require optional packages, model downloads, and CUDA assumptions.

References and Helpers

• references/forecasting-workflows.md gives long-term, M4, TimeXer, and zero-shot recipes.
• references/metrics-and-results.md explains forecast metrics and result files.
• references/troubleshooting.md covers forecasting-specific data/window/channel/model errors.
• scripts/build_tslib_command.py renders safe starter commands for long-term, M4, TimeXer, and zero-shot workflows.
• ../../references/model-catalog.md maps model names to optional dependency surfaces.

Avoid

• Do not treat zero-shot LTSM source files as verified just because core TSLib imports work.
• Do not run M4 or long-horizon benchmark scripts as a smoke check; use tiny data and short windows first.
• Do not use features MS without confirming --target and output channel expectations.