---
title: DataSynthis_ML_JobTask
emoji: 📈
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.49.0
app_file: app.py
pinned: false
license: mit
allow_internet: true
---

# Stock Price Forecasting App

This application uses three different models (ARIMA, Prophet, and LSTM) to forecast stock prices.

## ================================================================================
##  FINAL RECOMMENDATIONS
## ================================================================================

Based on the comprehensive evaluation:

1. BEST PERFORMING MODEL: LSTM
   - Lowest RMSE: $5.39

2. KEY FINDINGS:
   - ARIMA Model:
     * Simpler and faster to train
     * Better for short-term forecasts
     * Assumes linear relationships
     * RMSE: $28.98
     * MAPE: 11.57%

   - Prophet Model:
     * Excellent at capturing seasonality and trends
     * Handles missing data and outliers well
     * Provides uncertainty intervals
     * RMSE: $16.29
     * MAPE: 6.97%

   - LSTM Model:
     * Captures non-linear patterns
     * Better for complex time series
     * Requires more data and computation
     * RMSE: $5.39
     * MAPE: 2.06%

3. RECOMMENDATIONS:
   - For production deployment, consider ensemble methods combining all three models
   - Prophet is excellent for interpretability and trend analysis
   - LSTM performs well when sufficient training data is available
   - ARIMA provides quick baseline forecasts
   - Regularly retrain models with new data
   - Monitor prediction intervals and confidence bounds
   - Consider external factors (news, market sentiment) for better predictions

4. MODEL SELECTION GUIDE:
   - Use ARIMA for: Quick forecasts, baseline comparisons, stationary data
   - Use Prophet for: Seasonal patterns, interpretable results, business forecasts
   - Use LSTM for: Complex patterns, non-linear relationships, large datasets

5. LIMITATIONS:
   - Stock prices are inherently unpredictable
   - Past performance doesn't guarantee future results
   - Models should be used as decision support tools, not sole decision makers
   - Consider risk management and diversification strategies
   - All models assume patterns will continue into the future

## Features
- Real-time stock data fetching from Yahoo Finance
- Multiple forecasting models
- Interactive visualizations
- Customizable forecast periods

## Models
1. **ARIMA** - Traditional statistical model
2. **Prophet** - Facebook's time series forecasting
3. **LSTM** - Deep learning neural network

## Usage
1. Enter a stock ticker symbol (e.g., AAPL, GOOGL)
2. Select forecast period (1-90 days)
3. Choose which model(s) to use
4. Click "Generate Forecast"

⚠️ **Disclaimer**: For educational purposes only. Not financial advice.