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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/vmd decompose signals into oscillatory modes with VMD, hierarchical VMD, and multivariate support VMD Decomposition
foretools/fengineer automated feature engineering with transforms, interactions, MI selection, and RFECV Feature Engineering

Other foretools areas

Path Purpose
foretools/foreminer exploratory analysis and diagnostics
foretools/vmd decomposition tools
foretools/foraug augmentation-oriented utilities

How foretools fits the repo

  • foreblocks is the main model and training API.
  • foretools is a set of practical companion modules. Some are notebook-oriented and some are reusable library code.
  • foretools imports are deeper and less consolidated than foreblocks, so the safest entry points are the specific modules documented here.
  1. Time Series Generator if you need synthetic datasets or decomposition examples.
  2. BOHB Search if you need hyperparameter optimization outside the foreblocks.darts neural architecture search stack.
  3. VMD Decomposition if you need decomposition, denoising, or mode extraction workflows.
  4. Feature Engineering if you need automated feature construction, mutual information selection, or RFECV-based pruning.
  5. Repository Map if you want the broader code layout.