Repository Map
This page gives a quick path through the repository for contributors and power users.
Top-level areas
| Path |
Purpose |
README.md |
GitHub landing page |
mkdocs.yml |
Navigation and site structure for the /docs/ site |
web/ |
Static landing page assets for the published site root |
docs/ |
MkDocs source for the versioned documentation site |
examples/ |
Notebooks and runnable examples |
foreblocks/ |
Main forecasting library |
foretools/ |
Companion tooling |
foreblocks/
| Path |
Purpose |
foreblocks/__init__.py |
Top-level public exports |
foreblocks/core/ |
ForecastingModel, heads, conformal tools |
foreblocks/training/ |
Trainer and training support |
foreblocks/evaluation/ |
Evaluation and metrics |
foreblocks/ts_handler/ |
Preprocessing and sequence construction |
foreblocks/tf/ |
Transformer stack and advanced attention |
foreblocks/darts/ |
Neural architecture search |
foreblocks/mltracker/ |
Experiment tracking |
foreblocks/hybrid_mamba/ |
Hybrid Mamba SSM blocks (HybridMambaBlock, HybridMamba2Block, SSD) |
foreblocks/mamba/ |
Original Mamba backbone with MoE, positional encoding, and eval tools |
foreblocks/kan/ |
Kolmogorov-Arnold Network backbone |
| Path |
Purpose |
foretools/tsgen/ |
Synthetic time-series generation |
foretools/bohb/ |
BOHB, TPE configuration, pruning, and optimization plots |
foretools/foreminer/ |
Exploratory analysis and diagnostics |
foretools/fengineer/ |
Feature engineering utilities |
foretools/vmd/ |
Decomposition tools |
foretools/tsaug/ |
AutoDA-Timeseries: automated data augmentation with adaptive policy |
Recommended entry points by task
Related pages