foreblocks 0.1.9

Modular Time Series Forecasting Library

foreBlocks: Modular Deep Learning Library for Time Series Forecasting

foreBlocks is a flexible and modular deep learning library for time series forecasting, built on PyTorch. It provides a wide range of neural network architectures and forecasting strategies through a clean, research-friendly API — enabling fast experimentation and scalable deployment.

🔗 GitHub Repository · PyPI

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🚀 Quick Start

pip install foreblocks

Or install from source:

git clone https://github.com/lseman/foreblocks.git
cd foreblocks
pip install -e .

import torch
from foreblocks import (
    ForecastingModel, Trainer, TrainingConfig,
    create_dataloaders, LSTMEncoder, LSTMDecoder,
)

# Build a seq2seq LSTM forecaster
encoder = LSTMEncoder(input_size=8, hidden_size=128, num_layers=2)
decoder = LSTMDecoder(hidden_size=128, output_size=1, num_layers=2)

model = ForecastingModel(
    encoder=encoder,
    decoder=decoder,
    forecasting_strategy="seq2seq",
    target_len=24,
)

# Create data loaders
train_loader, val_loader = create_dataloaders(
    X_train, y_train, X_val, y_val, batch_size=32
)

# Train
config = TrainingConfig(num_epochs=100, learning_rate=1e-3, use_amp=True)
trainer = Trainer(model, config=config)
trainer.train(train_loader, val_loader)

---

✨ Key Features

Feature	Description
ForecastingModel	Unified model wrapping any encoder/decoder pair with pluggable heads
Modular Blocks	LSTM, GRU, Transformer, TCN, xLSTM, ODE, Graph, Fourier, Wavelet
Head System	13+ composable heads: RevIN, DAIN, Decomposition, PatchEmbed, FFT, Wavelet, Time2Vec, …
Forecasting Strategies	seq2seq, autoregressive, direct, transformer_seq2seq
Conformal Prediction	7 methods: split, local, rolling, agaci, enbpi, cptc, afocp
NAS via HeadComposer	Differentiable head search with configurable alpha LR and warmup
Trainer	AMP, gradient clipping, early stopping, LR scheduling, MLTracker SQLite logging
ModelEvaluator	MAE, RMSE, MAPE, SMAPE, MASE, coverage, sharpness, Winkler score

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🏗️ Architecture Overview

Input Tensor  ──┬──[input head]──► Encoder ──[encoder head]──►
                │                                               │
                │                                               ▼
                │                                     Attention Module
                │                                               │
                └──────────────────────────────────────────────►
                                                                │
                                                                ▼
                                                    Decoder ──[decoder head]──►
                                                                │
                                                      [output head / head_only]
                                                                │
                                                         Output Tensor

ForecastingModel glues any encoder, decoder, and attention module together and routes each forward pass through the chosen forecasting_strategy. Heads can be inserted at eight positions (input, encoder, attention, decoder, output, input_norm, output_norm, head) via add_head().

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🔮 Forecasting Strategies

LSTM Seq2Seq

from foreblocks import ForecastingModel, LSTMEncoder, LSTMDecoder

encoder = LSTMEncoder(input_size=8, hidden_size=128, num_layers=2)
decoder = LSTMDecoder(hidden_size=128, output_size=1, num_layers=2)

model = ForecastingModel(
    encoder=encoder, decoder=decoder,
    forecasting_strategy="seq2seq",
    target_len=24,
)

Transformer Seq2Seq

from foreblocks import ForecastingModel, TransformerEncoder, TransformerDecoder

encoder = TransformerEncoder(input_size=8, d_model=128, nhead=4, num_layers=3)
decoder = TransformerDecoder(d_model=128, output_size=1, nhead=4, num_layers=3)

model = ForecastingModel(
    encoder=encoder, decoder=decoder,
    forecasting_strategy="transformer_seq2seq",
    target_len=24,
)

Autoregressive & Direct

from foreblocks import ForecastingModel, LSTMEncoder

encoder = LSTMEncoder(input_size=8, hidden_size=128, num_layers=2)

# Autoregressive: step-by-step, feeding last prediction as next input
ar_model = ForecastingModel(
    encoder=encoder,
    forecasting_strategy="autoregressive",
    target_len=24, output_size=1,
)

# Direct: single-shot multi-step projection
direct_model = ForecastingModel(
    encoder=encoder,
    forecasting_strategy="direct",
    target_len=24, output_size=1,
)

---

⚙️ Advanced Features

Head System

from foreblocks.core.heads import RevinHead, PatchEmbedHead, FFTTopKHead

# Add a reversible instance normalisation head at the input
model.add_head(RevinHead(num_features=8), position="input")

# Stack patch embedding before the encoder
model.add_head(PatchEmbedHead(input_size=8, patch_size=16, d_model=128), position="encoder")

# FFT spectral filtering at the output
model.add_head(FFTTopKHead(top_k=16), position="output")

# Inspect all heads
print(model.list_heads())

Available heads: RevinHead, DAINHead, DecompositionHead, DifferencingHead, DropoutTSHead, FFTTopKHead, HaarWaveletTopKHead, LearnableFourierSeasonalHead, MultiKernelConvHead, MultiscaleConvHead, PatchEmbedHead, Time2VecHead, TimeAttentionHead

Neural Architecture Search (HeadComposer)

from foreblocks import TrainingConfig
from foreblocks.core.heads import HeadComposer, HeadSpec, RevinHead, DAINHead, PatchEmbedHead

# Define a search space
candidates = [RevinHead(8), DAINHead(8), PatchEmbedHead(8, 16, 128)]
composer = HeadComposer([HeadSpec(h) for h in candidates])
model.set_head_composer(composer)

# Enable NAS in training
config = TrainingConfig(
    num_epochs=100,
    train_nas=True,
    nas_alpha_lr=3e-4,
    nas_warmup_epochs=10,
)
trainer = Trainer(model, config=config)
trainer.train(train_loader, val_loader)

Conformal Prediction Intervals

from foreblocks import TrainingConfig, Trainer

config = TrainingConfig(
    conformal_enabled=True,
    conformal_method="enbpi",   # split | local | rolling | agaci | enbpi | cptc | afocp
    conformal_quantile=0.9,
)
trainer = Trainer(model, config=config)
trainer.train(train_loader, val_loader)

# Calibrate on held-out data, then predict with intervals
trainer.calibrate_conformal(cal_loader)
preds, lower, upper = trainer.predict_with_intervals(X_test)
coverage = trainer.compute_coverage(y_test, lower, upper)
trainer.plot_intervals(y_test, lower, upper)

Scheduled Sampling

from foreblocks.aux.utils import linear_schedule, exponential_schedule, inverse_sigmoid_schedule

# Linear decay: teacher forcing ratio goes from 1.0 → 0.0 over training
model = ForecastingModel(
    encoder=encoder, decoder=decoder,
    forecasting_strategy="seq2seq",
    target_len=24,
    scheduled_sampling_fn=linear_schedule(start=1.0, end=0.0, total_steps=100),
)

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🛠️ Configuration Reference

TrainingConfig

Parameter	Default	Description
num_epochs	100	Maximum training epochs
learning_rate	0.001	Optimiser learning rate
batch_size	32	Mini-batch size
patience	10	Early-stopping patience (epochs)
use_amp	True	Mixed-precision (AMP) training
gradient_clip_val	None	Gradient norm clipping value
scheduler_type	None	LR scheduler: cosine, step, plateau, …
weight_decay	0.0	L2 regularisation coefficient
train_nas	False	Enable differentiable NAS
nas_alpha_lr	3e-4	Architecture parameter learning rate
nas_warmup_epochs	5	Epochs before NAS search begins
conformal_enabled	False	Enable conformal prediction
conformal_method	"split"	Conformal method (split, local, rolling, agaci, enbpi, cptc, afocp)
conformal_quantile	0.9	Coverage target quantile

ModelConfig

Parameter	Description
input_size	Number of input features
hidden_size	Encoder/decoder hidden units
num_layers	Number of recurrent/transformer layers
output_size	Number of output targets
target_len	Forecast horizon
dropout	Dropout rate
forecasting_strategy	One of seq2seq, autoregressive, direct, transformer_seq2seq
model_type	One of lstm, transformer, informer-like, head_only
use_attention	Whether to add an attention module
nhead	Number of attention heads (Transformer)

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📖 Documentation

Resource	Link
Full Docs	lseman.github.io/foreblocks
Custom Blocks Guide	docs/custom_blocks.md
Transformer Guide	docs/transformer.md
Preprocessor Guide	docs/preprocessor.md
DARTS Guide	docs/darts.md
GitHub Repo	github.com/lseman/foreblocks
PyPI Package	pypi.org/project/foreblocks
Issue Tracker	GitHub Issues

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🩺 Troubleshooting

CUDA out of memory

Reduce batch_size in TrainingConfig, or enable gradient checkpointing:

config = TrainingConfig(batch_size=8, gradient_clip_val=1.0)

Alternatively, use use_amp=True (default) to reduce memory via mixed precision.

Loss is NaN during training

Common causes:

• Learning rate too high → lower learning_rate
• Inputs not normalised → add a RevinHead or DAINHead at position="input"
• Gradient explosion → set gradient_clip_val=1.0

model.add_head(RevinHead(num_features=input_size), position="input")
config = TrainingConfig(learning_rate=1e-4, gradient_clip_val=1.0)

Conformal intervals are too wide

Try a different conformal method or adjust the quantile:

config = TrainingConfig(
    conformal_enabled=True,
    conformal_method="agaci",   # adaptive method
    conformal_quantile=0.80,    # 80 % coverage
)

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💡 Best Practices

• Always normalise inputs — use RevinHead or DAINHead at position="input" unless your data is already z-scored.
• Start simple — begin with seq2seq + LSTM before exploring Transformer or head_only strategies.
• Use AMP — use_amp=True is the default; disable only when debugging NaN losses.
• Monitor with MLTracker — Trainer auto-creates a SQLite experiment log; pass auto_track=True (default).
• Validate conformal coverage — after calibration, call compute_coverage() and aim for ≥ your target quantile.
• NAS warmup — set nas_warmup_epochs to at least 5–10 % of num_epochs to stabilise base weights before architecture search.

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🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository and create a feature branch.
2. Run npm test / pytest and ensure all tests pass.
3. Follow existing code style (ruff / black).
4. Open a pull request with a clear description.

See CONTRIBUTING.md for detailed guidelines.

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📄 License

This project is licensed under the MIT License — see LICENSE.