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name: informer2020 description: "Use for Informer2020 long-sequence time-series forecasting training, evaluation, custom CSV preparation, and prediction workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Informer2020

Use this repo skill when a task involves the Informer2020 PyTorch implementation for long-sequence time-series forecasting: training/testing Informer or InformerStack, reproducing benchmark-style runs, preparing a custom time-series CSV, or running future prediction.

This is a source-style research repository, not a normal installable package. Treat the generated skill as the operating manual and use the bundled helpers below instead of reopening or executing original repo scripts directly.

First checks

1. Confirm the checkout contains the Informer2020 source modules (models, data, exp, utils) and the forecasting launcher.
2. Prepare a Python environment with PyTorch, NumPy, pandas, and the documented scientific stack. For exact historical reproduction, prefer the repository's legacy pins; for smoke validation, run the bundled helpers first.
3. Run a minimal import check from the checkout or with the checkout on PYTHONPATH:

python - <<'PY'
from models.model import Informer, InformerStack
from data.data_loader import Dataset_Custom, Dataset_Pred
print('Informer2020 imports OK')
PY

4. For a safe custom-data proof, generate and validate a tiny CSV before any long training run.

Route by task

User task	Read
Train/test Informer or InformerStack, choose attention/model lengths, adapt benchmark presets, inspect metrics/checkpoints, or debug runtime training failures	sub-skills/training-and-evaluation/SKILL.md
Prepare a custom CSV, choose S/M/MS, validate target/cols/freq, run do_predict, or debug data-loader/prediction output issues	sub-skills/custom-data-and-prediction/SKILL.md
Set up dependencies, understand legacy version pins, or decide CPU/CUDA behavior	references/install.md
Debug cross-cutting install, import, data, backend, or helper-script failures	references/troubleshooting.md
Check source snapshot and evidence paths before deciding whether the skill is stale	references/repo-provenance.md

Bundled helpers

• scripts/make_tiny_forecast_csv.py: create a deterministic custom forecasting CSV with a date column, covariates, and a target.
• scripts/check_forecast_csv.py: validate custom CSV columns, feature mode, row counts, and frequency before launch.
• scripts/run_forecasting_smoke.py: generate a tiny fixture and dry-run or execute a short train/test/predict smoke command from a checkout.

Use the helpers for validation and smoke checks; use the sub-skills for larger runs and task-specific interpretation.

Key behavior to remember

• The forecasting launcher trains and tests for every repeat; prediction is an extra branch enabled by do_predict.
• Built-in dataset names override CSV file name, target, and tensor widths. Custom data requires you to set dimensions yourself.
• M, S, and MS change both loader behavior and output width.
• The code auto-selects CUDA when visible. To force CPU reliably, hide CUDA or use the smoke helper's CPU backend option rather than relying on a string False value.
• Output directories are fingerprinted by the run setting. Checkpoints, metrics, test predictions, and future predictions are different files.

Avoid this skill when

• The task is about a different forecasting library or a general time-series theory question with no Informer2020 implementation surface.
• The task is repository maintenance unrelated to forecasting workflows; use a repository-maintenance skill instead.
• The user needs a production forecasting service, model registry, or MLOps deployment stack rather than this research implementation.