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---
---
language: en
tags:
- time-series
- forecasting
- lstm
- arima
- stock-market
license: mit
datasets:
- yahoo-finance
metrics:
- rmse
- mape
---
# LSTM Stock Price Forecasting
This repository contains an **LSTM model** trained on stock closing prices and compared with a traditional ARIMA baseline.
The goal is to forecast future stock values and evaluate which approach generalizes better.
---
## Dataset
- **Source:** Yahoo Finance
- **Ticker:** Apple Inc. (AAPL)
- **Period:** 2015–2023
- **Feature Used:** Daily closing price
---
## Models Implemented
- **ARIMA (Auto ARIMA)** — traditional statistical time-series forecasting
- **LSTM** — deep learning recurrent neural network for sequential data
---
## Evaluation Results
| Model | RMSE | MAPE |
|-------|-----------|----------|
| ARIMA | 15.7959 | 0.0857 |
| LSTM | 5.8747 | 0.0305 |
**Conclusion:** LSTM significantly outperforms ARIMA with lower RMSE and MAPE, showing its ability to capture nonlinear patterns in stock prices. Under a single split, LSTM significantly outperforms ARIMA.
---
### Rolling Window Evaluation
| Model | RMSE (avg) | MAPE (avg) |
|-------|------------|------------|
| ARIMA (Rolling Window) | 3.448 | 0.0304 |
| LSTM (Rolling Window) | 23.282 | 0.1869 |
Under rolling window evaluation, **ARIMA outperforms LSTM**, showing better stability and adaptability across multiple forecasting horizons.
---
## ARIMA vs LSTM Forecasts
**ARIMA Forecast:**
**LSTM Forecast:**
## Deployment
- Model hosted on **Hugging Face Hub**
- Repository: `Jalal10/DataSynthis_ML_JobTask`
- Includes model weights (`lstm_stock_model.h5`) and usage instructions
---
## Usage
```python
from huggingface_hub import hf_hub_download
import tensorflow as tf
# Download model
model_path = hf_hub_download(repo_id="Jalal10/DataSynthis_ML_JobTask", filename="lstm_stock_model.h5")
# Load model
model = tf.keras.models.load_model(model_path)
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