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 | 15.7959 | 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
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)