README.md

metadata

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.

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📊 Dataset

• Source: Yahoo Finance
• Ticker: Apple Inc. (AAPL)
• Period: 2015–2023
• Feature Used: Daily closing price

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🧠 Models Implemented

• ARIMA (Auto ARIMA) — traditional statistical time-series forecasting
• LSTM — deep learning recurrent neural network for sequential data

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📊 Evaluation Results

Model	RMSE	MAPE
ARIMA	15.796	0.0857
LSTM	7.533	0.0397

✅ Conclusion: LSTM significantly outperforms ARIMA with lower RMSE and MAPE, showing its ability to capture nonlinear patterns in stock prices.

---

📈 Example Forecast Plot

(You can upload and embed a forecast.png here if available)

![Forecast](./forecast.png)

## 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)