pre-process

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.

When to use / what to ask

Converts a CSV into pre-windowed train/val/test.npz for the pre_processed dataset type. Ask for:

1. Input — a single CSV (--input-csv, auto-split 0.7,0.1,0.2 by default) or a folder already holding train/val/test.csv (--input-dir)
2. Output dir and window sizes (--seq-len / --label-len / --pred-len)
3. Feature mode (M|S|MS, default M; S/MS also need --target, default OT)

Command

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 = ""

Notes

• Window sizes baked into the .npz must match the values used at training time.
• Scaler is always fitted on train only; with scaling on (default), set task.inverse = true in the run config to inverse-transform predictions.

Reference

Full flags (--split-ratio, --no-scale, output file contents): docs/en/pre-process.md.