---

name: statistical-baselines description: "Run and troubleshoot Naive, GBRT, ARIMA, and SARIMA statistical baseline workflows for long-term time-series forecasting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Statistical Baselines

Use this sub-skill when the task is to run, compare, smoke-test, or troubleshoot the repository's statistical long-forecasting baselines: Naive, GBRT, ARIMA, or SARIMA through run_stat.py and models/Stat_models.py.

Route here

• Run a repeat-last-value baseline (Naive, also described as Repeat-C / closest repeat in the source sweep notes).
• Run GBRT for a non-neural Gradient Boosting Regressor baseline.
• Run ARIMA or SARIMA, especially with a small --sample for expensive sampled comparisons.
• Adapt the long-forecast statistical sweep family from scripts/EXP-LongForecasting/Stat_Long.sh.
• Debug baseline-specific issues such as missing pmdarima, ARIMA/SARIMA slowness, per-batch sampling, or CSV feature/target mismatches.

Reroute

• Neural root models (Linear, DLinear, NLinear, Informer, Transformer, Autoformer) and weight visualization: use ../long-forecasting/SKILL.md.
• FEDformer Fourier/Wavelets workflows: use ../fedformer/SKILL.md.
• Pyraformer long-range, single-step, preprocessing, or TVM-related workflows: use ../pyraformer/SKILL.md.
• Common dataset layout questions that are not baseline-specific: use ../../references/data-layout.md when present.

Start fast

1. Confirm the requested model key is exactly one of Naive, GBRT, ARIMA, or SARIMA.

2. Confirm the CSV has a date column and the selected feature/target columns. For features=S or features=MS, set --target to an existing target column, commonly OT.

3. For a quick environment and data-path check, run the bundled smoke helper from this sub-skill directory or pass --repo-root explicitly:

   python scripts/smoke_stat_baselines.py --models Naive
   

4. For a single baseline run, prefer the bundled wrapper so paths, logging, sampling, and slow-model guards are explicit:

   python scripts/run_stat_baselines.py \
     --model Naive \
     --data custom \
     --data-root dataset \
     --data-path exchange_rate.csv \
     --features M \
     --seq-len 96 \
     --label-len 48 \
     --pred-len 96 \
     --batch-size 100
   

5. For ARIMA/SARIMA, start with sampled comparisons. The wrapper supplies conservative defaults when --sample is omitted, but pass --allow-slow only when deliberately running a large or full sample.

Bundled references

• references/cli-reference.md explains run_stat.py arguments, model behavior, output files, and sampling semantics.
• references/workflows.md gives single-run, smoke, source-sweep, and sampled comparison recipes.
• references/troubleshooting.md maps common dependency, slowness, sampling, data-layout, and output-location failures to fixes.

Bundled scripts

• scripts/run_stat_baselines.py wraps run_stat.py for single runs or a safe adaptation of the source statistical long-forecast sweep.
• scripts/smoke_stat_baselines.py creates a tiny synthetic custom CSV and runs one or more baseline models through the wrapper.