Foretools Overview
foretools is the companion toolbox that sits next to foreblocks.
Use foreblocks when you are building and training forecasting models. Use foretools when you need support utilities around that workflow: synthetic data, black-box search, exploratory diagnostics, decomposition, or feature engineering.
Best-documented tools
| Tool | When to use it | Docs |
|---|---|---|
foretools/tsgen | create synthetic series with known structure and ground-truth components | Time Series Generator |
foretools/bohb | run budgeted hyperparameter optimization with Hyperband + TPE | BOHB Search |
foretools/emd_like | decompose signals into oscillatory modes with VMD, EMD-family methods, hierarchical VMD, and multivariate support | VMD Decomposition |
foretools/fengineer | automated feature engineering with transforms, interactions, MI selection, and RFECV | Feature Engineering |
foretools/tsaug | data augmentation — jitter, scaling, time-warp, window-slice, and AutoDA search | AutoDA Augmentation |
Other foretools areas
foretools/foreminer
foreminer is an exploratory-analysis toolkit for understanding your time series before modelling.
Key capabilities:
- Changepoint detection — locate structural breaks in long series
- Cluster analysis — group series or windows by similarity
- Dimensionality diagnostics — PCA and UMAP projections of window embeddings
- Group-level summaries — aggregate statistics and seasonal decomposition across cohorts
- Stationarity checks — ADF and KPSS tests with automated reporting
Quick import path:
python
from foretools.foreminer import ForeMiner
miner = ForeMiner(series) # series: [T, D] numpy array
report = miner.run() # returns a dict of diagnostic frames
miner.plot_changepoints()
miner.plot_clusters(n_clusters=4)foreminer is primarily notebook-oriented. It does not expose a stable training-time API and is best used in exploratory phases before committing to a preprocessing and model pipeline.
foretools/foraug (tsaug)
Data augmentation utilities. See AutoDA Augmentation for the full guide.
How foretools fits the repo
foreblocksis the main model and training API.foretoolsis a set of practical companion modules. Some are notebook-oriented and some are reusable library code.foretoolsimports are deeper and less consolidated thanforeblocks, so the safest entry points are the specific modules documented here.
Recommended reading
- Time Series Generator if you need synthetic datasets or decomposition examples.
- BOHB Search if you need hyperparameter optimization outside the
foreblocks.dartsneural architecture search stack. - VMD Decomposition if you need decomposition, denoising, or mode extraction workflows.
- Feature Engineering if you need automated feature construction, mutual information selection, or RFECV-based pruning.
- Repository Map if you want the broader code layout.