Jalal10/DataSynthis_ML_JobTask

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	15.7959	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.

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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.

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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

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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)