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.
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 | 15.796 | 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
Below is an example forecast visualization (LSTM predictions vs actual stock prices):
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
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)