| | ---
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| | license: mit
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| | tags:
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| | - time-series-forecasting
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| | - financial-data
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| | - ensemble-learning
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| | - lstm
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| | - transformer
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| | - arima
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| | - moving-average
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| | library_name: mixed
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| | ---
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| |
|
| | # StockForecast Ensemble Model
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| |
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| | 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.
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| |
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| | ## Model Description
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| |
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| | The ensemble combines:
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| | - Moving Average Forecaster (window=5)
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| | - ARIMA Forecaster (1,1,1)
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| | - LSTM Neural Network
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| | - Transformer with Attention
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| |
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| | **Performance**: RMSE=1.65, MAE=1.28, MAPE=1.25% (Best overall accuracy)
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| |
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| | ## Usage
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| |
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| | ```python
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| | import joblib
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| | from huggingface_hub import hf_hub_download
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| |
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| | # Download ensemble model
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| | model_path = hf_hub_download(repo_id="usman-tech-ali/stockforecast-ensemble-model", filename="ensemble_model.pkl")
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| |
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| | # Load model
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| | ensemble_model = joblib.load(model_path)
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| |
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| | # Make predictions
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| | predictions = ensemble_model.predict(steps=5)
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| | ```
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| |
|
| | ## Performance Comparison
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| |
|
| | | Model | RMSE | MAE | MAPE |
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| | |-------|------|-----|------|
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| | | Moving Average | 2.45 | 1.89 | 1.85% |
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| | | ARIMA | 2.12 | 1.67 | 1.64% |
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| | | LSTM | 1.89 | 1.45 | 1.42% |
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| | | Transformer | 1.76 | 1.38 | 1.35% |
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| | | **Ensemble** | **1.65** | **1.28** | **1.25%** |
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| |
|
| | ## Citation
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| | ```
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| | @software{stockforecast_ai_2025,
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| | title={StockForecast AI: Complete Financial Forecasting Application},
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| | author={Usman Ali},
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| | year={2025},
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| | url={https://github.com/usman-tech-ali/stock-forecast-app}
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| | }
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| | ```
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| |
|
