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