> For the complete documentation index, see [llms.txt](https://docs.algenta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.algenta.ai/machine-learning/ml-model-selection.md).

# Time-series model selection

Walk-forward, purged, k-fold and temporal splits for leakage-free time-series model selection.

`model_selection` provides cross-validation splitters that respect time — the foundation for validating any model trained on sequential data (markets, sensors, demand). Random k-fold leaks the future into training; these splitters never do.

All functions run natively in the Mojo engine (zero Python imports, deterministic, seeded where applicable).

## The modules

| Module            | What it does                                                                                                                                                                                                     |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model_selection` | leakage-free cross-validation splitters for sequential data — walk-forward, purged, k-fold and temporal splits that respect time ordering instead of leaking the future into training the way random k-fold does |

## Functions

| Function                                                                 | What it does                                                                                                                                                                                                                                |
| ------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `walk_forward_splits(n, n_splits, min_train, expanding)`                 | Ordered walk-forward folds: train on `[0, k)`, test on the next contiguous window. `expanding=True` grows the training window each fold; `False` rolls it at `min_train`. Test windows are contiguous, non-overlapping, and cover the tail. |
| `purged_walk_forward_splits(n, n_splits, min_train, purge_gap, embargo)` | Finance-grade walk-forward: drops `purge_gap` samples between train end and test start (removes label leakage from overlapping forward-return windows) and embargoes `embargo` samples after each test window from later training folds.    |
| `kfold_splits(n, k, seed, shuffle)`                                      | Classic k-fold for non-temporal data.                                                                                                                                                                                                       |
| `stratified_kfold_splits(labels, k, seed)`                               | K-fold preserving class proportions per fold.                                                                                                                                                                                               |
| `train_test_split_temporal(n, test_fraction)`                            | Single ordered split.                                                                                                                                                                                                                       |

## When to use purged CV

If your label at time *t* is computed from data in *(t, t+h]* (a forward return, a settlement outcome), a plain walk-forward split lets training samples within *h* of the test boundary see part of the test window's future. Set `purge_gap >= h` to remove them. This is the standard Lopez de Prado correction for overlapping-label leakage.

## Verification

Every splitter is covered by known-value geometry tests plus a 50k-split invariant fuzzer.

**Next:** promote what you trained with [statistical model comparison](/machine-learning/ml-model-comparison.md), or browse the [engine catalog](/engine-runtime/catalog.md).


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