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---
license: apache-2.0
metrics:
- mse
tags:
- Timeseries
- Forecasting
- Energy
datasets:
- shivDwd/W_LSTMix_test_dataset
- ai-iot/EnergyBench
---
# W-LSTMix: A Hybrid Modular Forecasting Framework for Trend and Pattern Learning in Short-Term Load Forecasting
**W-LSTMix** is a lightweight, modular hybrid forecasting model designed for building-level load forecasting across diverse building types. With approximately **0.13 million parameters**, W-LSTMix combines:
- **Wavelet-based signal decomposition**
- **N-BEATS** for ensemble forecasting
- **LSTM** for gated memory
- **MLP-Mixer** for efficient patch-wise mixing
This model achieves high forecasting accuracy with a minimal computational footprint.
## 🚀 Features
- Hybrid Architecture Combining N-BEATS, LSTM and MLP-Mixer
- Lightweight: ~0.13M parameters and Edge-Deployable
- Modular design for flexible adaptation
- Effective generalization across building types
- Zero-shot capabilities
## 📓 Colab Quickstart
Use the following steps to try W-LSTMix on Google Colab:
```bash
!git clone https://github.com/shivDwd/W-LSTMix.git
%cd W-LSTMix
!git clone https://huggingface.co/datasets/shivDwd/W_LSTMix_test_dataset
!pip install -r requirements.txt
!python test.py
```
## 📊 Real-World Building Datasets
This model is trained on large-scale **real-world building energy datasets** from commercial and residential domains, collected from multiple countries.
| Dataset | Location | Type | # Buildings | # Observations | Years |
|-----------|--------------|-------------|-------------|----------------|-------------|
| IBlend | India | Commercial | 9 | 296,357 | 2013–2017 |
| Enernoc | USA | Commercial | 100 | 877,728 | 2012 |
| NEST | Switzerland | Residential | 1 | 34,715 | 2019–2023 |
| Ireland | Ireland | Residential | 20 | 174,398 | 2020 |
| MFRED | USA | Residential | 26 | 227,622 | 2019 |
| CEEW | India | Residential | 84 | 923,897 | 2019–2021 |
| SMART* | USA | Residential | 114 | 958,998 | 2016 |
| Prayas | India | Residential | 116 | 1,536,409 | 2018–2020 |
| NEEA | USA | Residential | 192 | 2,922,289 | 2018–2020 |
| SGSC | Australia | Residential | 13,735 | 172,277,213 | 2011–2014 |
| GoiEner | Spain | Residential | 25,559 | 632,313,933 | 2014–2022 |
**Total: 39,956 buildings and 812M+ hourly observations**
> ⚠️ These datasets are used under their respective terms/licenses for academic research only.
## 📈 Comparative Evaluation
We benchmark **W-LSTMix** against state-of-the-art Time Series Foundation Models (TSFMs) and N-BEATS under two broad settings: zero-shot and fine-tuning.
Please refer to the publication for a detailed summary of the results.
> **W-LSTMix: A Hybrid Modular Forecasting Framework for Trend and Pattern Learning in Short-Term Load Forecasting**
> *Shivam Dwivedi, Anuj Kumar, Harish Kumar Saravanan, Pandarasamy Arjunan*
> In *Proceedings of the 1st ICML Workshop on Foundation Models for Structured Data, Vancouver, Canada. 2025*
> [https://openreview.net/pdf?id=bG04Z3Jioc](https://openreview.net/pdf?id=bG04Z3Jioc)
To know more about W-LSTMix, please regfer to the official [Github](https://github.com/AI-IoT-Lab/W-LSTMix.git) repository.
## 📄 Citation
If you use W-LSTMix in your research or applications, please cite our paper:
```bibtex
@inproceedings{
dwivedi2025wlstmix,
title={W-{LSTM}ix: A Hybrid Modular Forecasting Framework for Trend and Pattern Learning in Short-Term Load Forecasting},
author={SHIVAM DWIVEDI and Anuj Kumar and Harish Kumar Saravanan and Pandarasamy Arjunan},
booktitle={1st ICML Workshop on Foundation Models for Structured Data},
year={2025},
url={https://openreview.net/forum?id=bG04Z3Jioc}
} |