foreblocks 0.1.19

Modular Time Series Forecasting Library

foreBlocks

foreBlocks is a modular PyTorch toolkit for time-series forecasting, experiment management, and companion utilities.

This repository is structured as two cooperating packages:

• foreblocks: forecasting models, training, evaluation, preprocessing, DARTS search, and conformal uncertainty.
• foretools: companion utilities for synthetic data, feature engineering, decomposition, and hyperparameter search.

The recommended workflow is:

1. start with the stable top-level public API in foreblocks
2. validate one small training loop end to end
3. add preprocessing, search, or specialist tooling only when the baseline path works

Install

This package requires Python 3.10 or newer.

Core install

pip install foreblocks

Optional extras

Extra	Adds
preprocessing	TimeSeriesHandler, windowing, scaling, filtering, imputation, and time-feature generation
darts	DARTS architecture search, evaluation, and NAS dependencies
mltracker	experiment tracking API, local dashboard, and CLI TUI
studio	Studio frontend launcher and bundled server command
vmd	VMD decomposition, search support, and analysis helpers
wavelets	wavelet preprocessing and attention-head utilities
benchmark	external forecasting baselines and spreadsheet readers
foreminer	changepoint detection, dataset mining, and analysis utilities
all	all runtime extras above

Examples:

pip install "foreblocks[darts]"
pip install "foreblocks[mltracker]"
pip install "foreblocks[studio]"
pip install "foreblocks[vmd,wavelets]"
pip install "foreblocks[all]"

Local development install

git clone https://github.com/lseman/foreblocks.git
cd foreblocks
pip install -e ".[dev]"

Launch the Studio frontend

foreblocks-studio

By default, this opens a browser on 127.0.0.1 or localhost.

Optional flags:

foreblocks-studio --open
foreblocks-studio --no-open
foreblocks-studio --host 0.0.0.0 --port 8080

Quickstart

The smallest reliable path is a direct forecasting model with a custom head. This path avoids extra dependencies and verifies that the public API is wired correctly.

import numpy as np
import torch
import torch.nn as nn

from foreblocks import (
    ForecastingModel,
    ModelEvaluator,
    Trainer,
    TrainingConfig,
    create_dataloaders,
)

seq_len = 24
horizon = 6
n_features = 4

rng = np.random.default_rng(0)
X_train = rng.normal(size=(64, seq_len, n_features)).astype("float32")
y_train = rng.normal(size=(64, horizon)).astype("float32")
X_val = rng.normal(size=(16, seq_len, n_features)).astype("float32")
y_val = rng.normal(size=(16, horizon)).astype("float32")

train_loader, val_loader = create_dataloaders(
    X_train,
    y_train,
    X_val,
    y_val,
    batch_size=16,
)

head = nn.Sequential(
    nn.Flatten(),
    nn.Linear(seq_len * n_features, 64),
    nn.GELU(),
    nn.Linear(64, horizon),
)

model = ForecastingModel(
    head=head,
    forecasting_strategy="direct",
    model_type="head_only",
    target_len=horizon,
)

trainer = Trainer(
    model,
    config=TrainingConfig(
        num_epochs=5,
        batch_size=16,
        patience=3,
        use_amp=False,
    ),
    auto_track=False,
)

history = trainer.train(train_loader, val_loader)
evaluator = ModelEvaluator(trainer)
metrics = evaluator.compute_metrics(torch.tensor(X_val), torch.tensor(y_val))

print(history.train_losses[-1], metrics)

Why this path

• validates that the import surface works
• checks dataloader shapes and model output sizes
• avoids optional subsystems during the first run
• keeps the first success criterion small and confirmable

If you start from a raw [T, D] time series instead of windows, use TimeSeriesHandler from foreblocks.ts_handler after installing foreblocks[preprocessing].

Public API

The most stable first imports are exposed from the top-level foreblocks package:

Import	Purpose
ForecastingModel	Core forecasting wrapper for direct, autoregressive, and seq2seq-style models
Trainer	Training loop with NAS hooks, MLTracker integration, and optional conformal support
ModelEvaluator	Prediction helpers, metrics, cross-validation, and training-curve plots
TimeSeriesHandler	Raw-series preprocessing, windowing, scaling, and imputation bridge
TimeSeriesDataset	Dataset wrapper used by the dataloader helper
create_dataloaders	Build train/validation PyTorch dataloaders from NumPy arrays
ModelConfig, TrainingConfig	Lightweight configuration dataclasses
LSTMEncoder, LSTMDecoder, GRUEncoder, GRUDecoder	Recurrent encoder/decoder blocks
TransformerEncoder, TransformerDecoder	Transformer backbones and related advanced features
AttentionLayer	Attention module entry point

Repository map

Path	What it contains
foreblocks/core	ForecastingModel, heads, conformal utilities, sampling
foreblocks/training	Trainer, training loop, quantization utilities
foreblocks/evaluation	ModelEvaluator, benchmarking helpers
foreblocks/ts_handler	TimeSeriesHandler, imputation, filtering, outlier handling
foreblocks/tf	transformer stack, attention variants, MoE, norms, embeddings
foreblocks/darts	neural architecture search pipeline and evaluation
foreblocks/mltracker	experiment tracking server, logging, and TUI integration
foretools	synthetic time series, BOHB search, feature engineering, decomposition
examples/	runnable demos and notebooks
web/	static landing page assets for the published site root
docs/	VitePress source for the documentation site

Documentation map

Start here if you are new to the repository:

• Documentation Overview
• Getting Started
• Docs home

Topic guides:

• Preprocessor Guide
• Custom Blocks Guide
• Transformer Guide
• MoE Guide
• DARTS Guide
• Troubleshooting

Companion tooling:

• Foretools Overview
• Time Series Generator
• BOHB Search
• VMD Decomposition

Examples and notebooks:

• examples/adaptive_mrmr_demo.py
• foretools/tsgen/ts_gen_complete_series.ipynb
• foretools/tsgen/ts_gen_doc.ipynb
• examples/

There is a repository-local docs navigation file at docs/.vitepress/config.js.

Current project status

• The repository is broad and still evolving. Some subsystems are more mature than others.
• The top-level imports listed above are the safest place to start.
• Trainer supports MLTracker and conformal prediction; use auto_track=False during local smoke tests.
• Decoder-based seq2seq and transformer workflows have stricter dimension contracts than the direct forecasting path.
• TrainingConfig now centralizes trainer, NAS, MLTracker, and conformal settings.

Contributing

Documentation improvements are especially valuable here because foreblocks spans forecasting models, search, preprocessing, and auxiliary tooling. If you add or change a public API, update:

1. this README.md
2. the relevant guide under docs/
3. at least one runnable example or notebook