foreblocks 0.1.17

Modular Time Series Forecasting Library

foreBlocks

foreBlocks is a modular PyTorch library for time-series forecasting. The repository combines:

• foreblocks: forecasting models, training, evaluation, preprocessing, and DARTS search
• foretools: companion utilities, synthetic data generation, decomposition, and analysis notebooks

The project is best approached as a research toolkit rather than a single monolithic framework. The most stable public entry points are the top-level imports exported from foreblocks.

Install

pip install foreblocks

Install optional extras when you need specific subsystems:

Extra	Adds
darts	DARTS search plus analysis dependencies
mltracker	experiment tracking API and UI dependencies
studio	bundled Studio frontend launcher command
vmd	VMD decomposition and Optuna-based search support
wavelets	optional wavelet backends
benchmark	external forecasting baselines and spreadsheet readers
foreminer	changepoint-detection support
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]"

Launch the bundled Studio frontend:

foreblocks-studio

By default, this auto-opens a browser when serving 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

Local development install:

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

Validated Quickstart

The example below is intentionally small and uses the most reliable path through the current API: a direct forecaster with a custom head, trained through Trainer.

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)

This path was smoke-tested in the repository. Once that is working, move on to encoder/decoder models, preprocessing, and DARTS.

Public API

These are the top-level imports currently exposed by foreblocks:

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	Time-series handling pipeline for windowing, scaling, filtering, imputation, and time features
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
foretools/tsgen	synthetic time-series generator and notebooks
examples/	notebooks and runnable usage examples
web/	static landing page assets for the published site root
docs/	MkDocs source for the versioned documentation site published under /docs/

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

Useful notebooks and examples:

• Synthetic Series Notebook
• TS Generator Documentation Notebook
• AdaptiveMRMR Demo
• Example notebooks

There is also a repository-local docs navigation file at mkdocs.yml. The current publishing model is:

• site root /: custom landing page from web/index.html
• site docs /docs/: MkDocs site built from docs/

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, but you can disable tracking during local smoke tests with auto_track=False.
• MultiAttention now includes an experimental attention-matching KV compaction mode for dense paged causal decode. Enable it with use_attention_matching_compaction=True and use_mla=False.
• For decoder-based seq2seq and transformer workflows, use the topic guides before wiring custom modules, because dimension contracts are stricter than the direct head path.
• TrainingConfig now lives in a single canonical location and includes trainer, NAS, MLTracker, and conformal settings.

Contributing

Documentation improvements are especially valuable here because the repository 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