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Pre-process a dataset into pre-windowed .npz files for use with the ModernTSF `pre_processed` dataset type. Use when the user wants to convert CSV data to .npz format before training, or to set up a new dataset with pre-computed windows.
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SOC 職業分類に基づく
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 | pre-process |
| description | Pre-process a dataset into pre-windowed .npz files for use with the ModernTSF `pre_processed` dataset type. Use when the user wants to convert CSV data to .npz format before training, or to set up a new dataset with pre-computed windows. |
Converts a CSV into pre-windowed train/val/test.npz for the pre_processed dataset type. Ask for:
--input-csv, auto-split 0.7,0.1,0.2 by default) or a folder already holding train/val/test.csv (--input-dir)--seq-len / --label-len / --pred-len)M|S|MS, default M; S/MS also need --target, default OT)uv run python tool/pre_process.py \
--input-csv dataset/ETT-small/ETTh1.csv \
--output-dir dataset/ETTh1_npy \
--seq-len 512 --label-len 0 --pred-len 96 --features M
Then point a dataset config at the output:
[dataset]
name = "pre_processed"
root_path = "<output-dir>"
data_path = ""
.npz must match the values used at training time.task.inverse = true in the run config to inverse-transform predictions.Full flags (--split-ratio, --no-scale, output file contents): docs/en/pre-process.md.