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Jalal10/
DataSynthis_ML_JobTask

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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)