---
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}
}