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name: ltsf-linear description: "Routes the repo's long-term forecasting, statistical baselines, FEDformer, and Pyraformer workflows, plus shared setup and data-layout checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LTSF-Linear

This is the root router for the LTSF-Linear repository. Use it when the task is about the repo as a whole, when you need shared setup or data checks, or when you need to choose between the repo's four major workflow families.

Read first

• references/repo-provenance.md when checking whether this skill matches the current checkout or before refreshing it.
• references/data-layout.md for the shared CSV and dataset-directory conventions.
• references/model-overview.md for the quick model-family map.
• references/cli-reference.md for the main entry points and the most useful flags.
• references/workflows.md for common run patterns and route selection.
• references/troubleshooting.md for cross-cutting install, import, backend, and data-layout failures.
• scripts/check_env.py for a fast import and CUDA readiness check.
• scripts/check_data_layout.py for a small dataset-layout sanity check.

Route map

• sub-skills/long-forecasting/SKILL.md — Linear, DLinear, NLinear, Informer, Transformer, Autoformer, benchmark sweeps, and weight plotting.
• sub-skills/statistical-baselines/SKILL.md — Naive, GBRT, ARIMA, and SARIMA baselines.
• sub-skills/fedformer/SKILL.md — the FEDformer subrepo and its Fourier / Wavelets variants.
• sub-skills/pyraformer/SKILL.md — Pyraformer long-range forecasting, single-step forecasting, preprocessing, and synthetic data generation.

Install and preflight

1. Create a Python environment with the repo's runtime stack. The verified inspection environment used a CUDA-capable PyTorch 1.9.0+cu111 build on x86_64 Linux.

2. Install the root requirements first, then add route-specific extras only when you need them:

   • root requirements.txt
   • pmdarima for statistical baselines
   • sympy and einops for FEDformer Wavelets
   • fbm for Pyraformer synthetic generation
   • Pyraformer/requirements.txt for Pyraformer routes

3. Run a fast shared smoke before any heavier workflow:

   python scripts/check_env.py --scope all --device auto
   

4. Run a quick layout check for the dataset you expect to use:

   python scripts/check_data_layout.py --kind root --data-root dataset --data-path exchange_rate.csv
   

Root workflow hints

• The main benchmark launcher is run_longExp.py.
• The statistical baseline launcher is run_stat.py.
• The FEDformer launcher lives in FEDformer/run.py.
• The Pyraformer launchers live in Pyraformer/long_range_main.py and Pyraformer/single_step_main.py.
• run_longExp.py uses a fragile --use_gpu boolean parser; prefer the bundled wrapper scripts when you want explicit CPU or CUDA control.
• run_stat.py imports pmdarima at module import time, so missing that dependency fails even --help.
• FEDformer's parser default model is not a valid FEDformer family name, so always pass an explicit --model.
• Pyraformer's optional TVM path is separate from the minimum smoke surface.

When to stay at the root

Use the root skill and the shared scripts when you only need:

• a quick environment or import check,
• dataset/path validation,
• a model-family decision,
• or a high-level routing answer before choosing a sub-skill.

Move to a sub-skill once the user has picked a concrete workflow family.