base-attentive 2.0.0rc1

Kernel for probabilistic forecasting with attentive sequence models.

A modular encoder-decoder architecture for sequence-to-sequence time series forecasting with layered attention mechanisms.

Overview

BaseAttentive is a modular encoder-decoder architecture designed to process three distinct types of inputs:

• Static features — constant across time (e.g., geographical coordinates, site properties)
• Dynamic past features — historical time series (e.g., sensor readings, observations)
• Known future features — forecast-period exogenous variables (e.g., weather forecasts)

It combines these inputs using a configurable attention stack and can serve as a building block for models such as HALNet and PIHALNet.

Key Features

Architecture options

• Hybrid mode: Multi-scale LSTM + Attention (objective="hybrid")
• Transformer mode: Pure self-attention (objective="transformer")
• Operational shortcuts: TFT-like (mode="tft"), PIHALNet-like (mode="pihal")
• Declarative attention stack via attention_levels

Core components

• Variable Selection Networks (VSN) for learnable feature weighting
• Multi-scale LSTM for hierarchical temporal patterns (scales, multi_scale_agg)
• Cross, hierarchical, and memory-augmented attention
• Transformer encoder/decoder blocks
• Quantile and probabilistic forecast heads

V2 system

• BaseAttentiveSpec / BaseAttentiveComponentSpec for backend-neutral config
• ComponentRegistry and ModelRegistry for pluggable components
• BaseAttentiveV2Assembly resolver/assembler pattern
• Multi-backend: TensorFlow (stable), JAX, PyTorch (experimental)

Runtime support

• Keras 3 multi-backend implementation
• make_fast_predict_fn for traced TF inference
• Input validation utilities

Installation

pip install base-attentive

Backend extras

pip install "base-attentive[tensorflow]"   # TensorFlow backend (stable)
pip install "base-attentive[jax]"          # JAX backend (experimental)
pip install "base-attentive[torch]"        # PyTorch backend (experimental)
pip install "base-attentive[all-backends]" # All backends

From source

git clone https://github.com/earthai-tech/base-attentive.git
cd base-attentive
pip install -e ".[dev,tensorflow]"

Development Setup

If you use make (Linux, macOS, WSL, or Git Bash on Windows), the repository includes a Makefile with common development commands:

make install-tensorflow   # editable install with dev + TensorFlow extras
make test-fast            # quick local pytest pass
make lint                 # Ruff lint + format check
make format               # apply Ruff fixes and formatting
make build                # build wheel and sdist

Run make help to see the full command list.

Quick Start

import numpy as np
from base_attentive import BaseAttentive

# Create a model
model = BaseAttentive(
    static_input_dim=4,           # 4 static features
    dynamic_input_dim=8,          # 8 dynamic features in history
    future_input_dim=6,           # 6 known future features
    output_dim=2,                 # 2 target variables
    forecast_horizon=24,          # 24-step ahead forecast
    quantiles=[0.1, 0.5, 0.9],   # Uncertainty quantiles
    embed_dim=32,
    num_heads=8,
    dropout_rate=0.15,
)

# Prepare inputs
BATCH_SIZE = 32
x_static  = np.random.randn(BATCH_SIZE, 4).astype("float32")
x_dynamic = np.random.randn(BATCH_SIZE, 100, 8).astype("float32")  # 100 history steps
x_future  = np.random.randn(BATCH_SIZE, 24, 6).astype("float32")   # 24 forecast steps

# Make predictions
predictions = model([x_static, x_dynamic, x_future])
print(predictions.shape)  # (32, 24, 3, 2) — [batch, horizon, quantiles, outputs]

Architecture Configuration

Override defaults via architecture_config:

from base_attentive import BaseAttentive

model = BaseAttentive(
    static_input_dim=4,
    dynamic_input_dim=8,
    future_input_dim=6,
    output_dim=2,
    forecast_horizon=24,
    mode="tft",                          # TFT-like shortcut
    attention_levels=["cross", "hierarchical"],
    scales=[1, 2, 4],                    # Multi-scale LSTM strides
    multi_scale_agg="average",
    architecture_config={
        "encoder_type": "transformer",   # Pure attention encoder
        "feature_processing": "vsn",     # Variable selection networks
    },
)

Available architecture_config keys:

• encoder_type: 'hybrid' (LSTM+Attention) or 'transformer' (pure attention)
• feature_processing: 'vsn' (learnable selection) or 'dense' (standard layers)

Documentation

Full documentation: https://base-attentive.readthedocs.io

Papers & References

• Attention Is All You Need (Vaswani et al., 2017)
• Temporal Fusion Transformers (Lim et al., 2021)
• Neural Machine Translation by Jointly Learning to Align and Translate (Bahdanau et al., 2015)

License

This project is licensed under the Apache License 2.0 — see LICENSE for details.

Citation

@software{baseattentive2026,
  author  = {Kouadio, L.},
  title   = {BaseAttentive: Modular Multi-Backend Encoder-Decoder Architecture for Probabilistic Time Series Forecasting},
  year    = {2026},
  version = {2.0.0rc1},
  url     = {https://github.com/earthai-tech/base-attentive}
}

Contributing

Contributions are welcome! Please open an issue or submit a pull request. See the Contributing Guide for details.

Acknowledgments

• Built on Keras 3 with TensorFlow-first support and experimental JAX/PyTorch paths
• Inspired by recent time series forecasting research (TFT, PIHALNet, HALNet)