Update README.md

README.md

@@ -27,6 +27,18 @@ This model achieves high forecasting accuracy with a minimal computational footp
 - 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.
@@ -59,3 +71,17 @@ Please refer to the publication for a detailed summary of the results.
 > [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}
+}