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Extract TFB-style statistical characteristics (trend strength, seasonality strength, stationarity) from a dataset. Use when the user wants to profile, describe, or quantify the trend/seasonality/stationarity of a time-series dataset before benchmarking.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Add a new model to ModernTSF by scaffolding it with `tool/tsf.py new-model`, filling the architecture, and verifying with `tsf smoke`. Use when the user wants to integrate a new PyTorch model, register a custom architecture, or wire a new forecaster into the benchmark pipeline.
Verify model(s) end-to-end by running their smoke configs concurrently and reporting PASS/FAIL via `tool/tsf.py smoke`. Use when the user wants to check that a model (new or existing) trains and produces the right output shape, validate a port, or run a quick CI-style gate over many models.
Understand a model in ModernTSF by reading its README card as progressive disclosure — paper venue/date/arXiv/abstract first, then source only if needed. Use when the user asks "what is model X", "tell me about X", "了解 X 模型", "how does X work", which paper is X from, or wants to compare/learn what a forecaster does before running it.
Add or run a *probabilistic* forecasting model in ModernTSF — one that outputs quantiles or a distribution (prediction intervals / uncertainty) instead of a single point, and is scored with CRPS / WQL / coverage. Use this whenever the task involves prediction intervals, quantiles, uncertainty, a probabilistic or distributional forecast, pinball / quantile loss, Gaussian NLL, or the metrics `crps` / `wql` / `coverage_80` / `width_80` — even if the user only says "predict a range" or "give me uncertainty bands". Do NOT use the plain `add-model` flow for these: it builds a point model. Probabilistic output is a separate `output_type` axis, orthogonal to `task.mode`.
Package a finished run into a TSEval Submission Report (result + agent trajectory + report) and contribute it via a GitHub PR to the TSEval leaderboard repo, via `tsf trace` / `tsf submit`. Use when the user wants to submit results to the TSEval leaderboard, capture an experiment trajectory, or build a leaderboard from submissions.
Diagnose and report a ModernTSF framework defect upstream, with user approval, as a GitHub issue or a small verified PR against Diaugeia/ModernTSF. Use after reproducing a crash in src/ or tool/, wrong output or shapes, a broken config or registry, a doc/CLI mismatch, or another repository defect rather than a problem in the user's data or code.
| name | characteristics |
| description | Extract TFB-style statistical characteristics (trend strength, seasonality strength, stationarity) from a dataset. Use when the user wants to profile, describe, or quantify the trend/seasonality/stationarity of a time-series dataset before benchmarking. |
Profile a dataset's statistical properties — how trended, how seasonal, how stationary — before choosing models, or to report dataset-level stats. Works with any dataset config the project supports (single-file, custom, presplit, pre-processed, traffic bundles).
uv run python tool/dataset_characteristics.py \
--config configs/datasets/etth1.toml --split train --per-channel
Outputs trend_strength and seasonality_strength (STL-style, 0–1) plus stationarity (ADF p-value via statsmodels) to work_dirs/<dataset>/characteristics_<split>.csv. --period N overrides the FFT-estimated seasonal period; omit --per-channel for dataset-level rows only.
Full flags and metric definitions: docs/en/dataset-characteristics.md.