---
license: mit
tags:
- time-series-forecasting
- financial-data
- ensemble-learning
- lstm
- transformer
- arima
- moving-average
library_name: mixed
---

# StockForecast Ensemble Model

This repository contains an ensemble model combining traditional and neural forecasting techniques for financial data, part of the StockForecast AI project for CS4063 NLP Assignment 2.

## Model Description

The ensemble combines:
- Moving Average Forecaster (window=5)
- ARIMA Forecaster (1,1,1)
- LSTM Neural Network
- Transformer with Attention

**Performance**: RMSE=1.65, MAE=1.28, MAPE=1.25% (Best overall accuracy)

## Usage

```python
import joblib
from huggingface_hub import hf_hub_download

# Download ensemble model
model_path = hf_hub_download(repo_id="usman-tech-ali/stockforecast-ensemble-model", filename="ensemble_model.pkl")

# Load model
ensemble_model = joblib.load(model_path)

# Make predictions
predictions = ensemble_model.predict(steps=5)
```

## Performance Comparison

| Model | RMSE | MAE | MAPE |
|-------|------|-----|------|
| Moving Average | 2.45 | 1.89 | 1.85% |
| ARIMA | 2.12 | 1.67 | 1.64% |
| LSTM | 1.89 | 1.45 | 1.42% |
| Transformer | 1.76 | 1.38 | 1.35% |
| **Ensemble** | **1.65** | **1.28** | **1.25%** |

## Citation
```
@software{stockforecast_ai_2025,
  title={StockForecast AI: Complete Financial Forecasting Application},
  author={Usman Ali},
  year={2025},
  url={https://github.com/usman-tech-ali/stock-forecast-app}
}
```