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README.md

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+---
+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}
+}
+```

config.json

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+{
+  "model_type": "ensemble_forecaster",
+  "models": [
+    "moving_average",
+    "arima",
+    "lstm",
+    "transformer"
+  ],
+  "framework": "mixed",
+  "task": "time-series-forecasting",
+  "dataset": "financial_ohlcv",
+  "metrics": {
+    "ensemble": {
+      "rmse": 1.65,
+      "mae": 1.28,
+      "mape": 1.25
+    }
+  }
+}

ensemble_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0a0deec56937f2063470afebe4b7d0f564ec3465eed611fd121e262aead0c4db
+size 636031