---
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.