Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+metrics:
+- mse
+tags:
+- Timeseries
+- Forecasting
+- Energy
+---
+
+# 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
+
+## 📊 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.