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---
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license: apache-2.0
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datasets:
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- dirganmdcp/yfinance_Indonesia_Stock_Exchange
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language:
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- en
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metrics:
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- mape
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pipeline_tag: time-series-forecasting
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library_name: keras
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tags:
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- time-series
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- stock-forecasting
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- LSTM
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- ARIMA
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- Prophet
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- machine-learning
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- deep-learning
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- forecasting
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---
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# 📈 Stock Price Forecasting - DataSynthis ML Job Task |
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This repository contains implementations of **time-series forecasting** for stock prices using both **traditional statistical models (ARIMA, Prophet)** and **deep learning (LSTM)**. |
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The project demonstrates model comparison, rolling-window evaluation, and deployment to Hugging Face Hub. |
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## Project Overview |
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- **Dataset**: Daily stock price dataset (closing prices). |
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- **Models Implemented**: |
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- ARIMA (AutoRegressive Integrated Moving Average) |
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- Prophet (Additive Time Series Forecasting by Meta) |
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- LSTM (Long Short-Term Memory Neural Network) |
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- **Evaluation**: |
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- Rolling-window forecasts |
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- Metrics: RMSE, MAPE |
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- **Deployment**: |
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- Models and results shared on Hugging Face Hub. |
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## Repository Contents |
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- `lstm_model.h5` – Trained LSTM model |
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- `scaler.pkl` – Scaler used for preprocessing |
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- `performance_summary.csv` – Comparison of ARIMA, Prophet, and LSTM performance |
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- `stock_forecasting_notebook.ipynb` – Full notebook with preprocessing, training, evaluation, and plots |
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- `upload_to_hf.py` – Script for uploading to Hugging Face Hub |
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## Quick start |
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1. Create and activate a python environment (recommended: conda or venv) |
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```bash |
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python -m venv venv |
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source venv/bin/activate # Linux/macOS |
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venv\Scripts\activate # Windows |
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pip install -r requirements.txt |
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``` |
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2. Start Jupyter and open the notebook: |
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```bash |
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jupyter notebook stock_forecasting_notebook.ipynb |
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``` |
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3. The notebook contains cells to download real stock data via `yfinance` (if you have internet) or use the included `sample_stock.csv` for an offline demo. |
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## Hugging Face deployment (notes) |
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- Use `upload_to_hf.py` to push saved model files to the HF repo `DataSynthis_ML_JobTask` after creating it on the Hugging Face website (or the script will create the repo for you if you provide a valid token). |
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- Create a HF token at https://huggingface.co/settings/tokens and set environment variable `HF_TOKEN` or pass `--token` to the script. |
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## Results |
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The performance of the three models on stock price forecasting is summarized below: |
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| Model | RMSE | MAPE (%) | |
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|---------|--------|----------| |
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| ARIMA | 3.3748 | 1.8973 | |
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| Prophet | 4.7650 | 3.1859 | |
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| LSTM | 2.0890 | 1.2516 | |
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### Key Insights |
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- **LSTM** achieved the **lowest RMSE and MAPE**, showing the best accuracy. |
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- **ARIMA** performed reasonably well, but less effective with non-linear trends. |
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- **Prophet** captured trends and seasonality but had higher errors. |
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- Overall, **LSTM is the most reliable model** for this task. |
