TIME SERIES FORECASTING • ENSEMBLE LEARNING

Multi-Model Time Series Forecasting System

Advanced ensemble forecasting system combining LSTM neural networks, XGBoost, and Prophet models for stock market predictions with 98.2% accuracy and $2.01 RMSE on 30-day forecasts.

View Live Demo Model Documentation
LSTMXGBoostProphetEnsemble MethodsTime Series AnalysisPythonTensorFlow

98.2%

Model Accuracy

Ensemble approach combining three models

$2.01

RMSE

Root Mean Squared Error on predictions

96%

Confidence Intervals

For risk assessment and uncertainty quantification

Technical Implementation

Ensemble Architecture

Developed a sophisticated ensemble system that combines the strengths of three complementary forecasting approaches: LSTM for capturing long-term dependencies, XGBoost for handling non-linear relationships, and Prophet for modeling seasonality and trends.

  • •LSTM Neural Network: 3-layer architecture with 128, 64, and 32 units, dropout regularization (0.2), trained on 60-day sequences
  • •XGBoost Model: Gradient boosting with 500 estimators, max depth 7, learning rate 0.01, optimized for time series features
  • •Prophet Model: Facebook's time series forecasting tool with custom seasonality components and holiday effects
  • •Ensemble Strategy: Weighted averaging with dynamic weights based on recent performance (LSTM: 40%, XGBoost: 35%, Prophet: 25%)

Feature Engineering

Comprehensive feature engineering pipeline creating 50+ technical indicators and time-based features to capture market dynamics and improve prediction accuracy.

  • •Technical Indicators: Moving averages (SMA, EMA), RSI, MACD, Bollinger Bands, volume indicators
  • •Temporal Features: Day of week, month, quarter, trading days, holiday indicators
  • •Lag Features: 1, 3, 7, 14, and 30-day lagged values for price and volume
  • •Rolling Statistics: 7, 14, and 30-day rolling mean, std, min, max for volatility analysis

Model Performance Metrics

ModelAccuracyRMSEMAER²
LSTM Neural Network97.8%$2.15$1.680.956
XGBoost97.5%$2.28$1.820.948
Prophet96.9%$2.45$1.950.941
Ensemble Model98.2%$2.01$1.540.964

Dataset & Training

  • •Data Source: 10+ years of historical stock market data (2013-2024) from Yahoo Finance API
  • •Training Set: 80% of data (2013-2021) for model training and hyperparameter tuning
  • •Validation Set: 10% of data (2021-2022) for model selection and ensemble weight optimization
  • •Test Set: 10% of data (2022-2024) for final performance evaluation and backtesting
  • •Cross-Validation: Time series cross-validation with expanding window to prevent data leakage

Key Findings & Insights

  • •Ensemble Superiority: Ensemble model outperformed individual models by 0.4-1.3% in accuracy, demonstrating the value of model diversity
  • •LSTM Strength: LSTM excelled at capturing long-term trends and momentum, especially during stable market conditions
  • •XGBoost Adaptability: XGBoost performed best during volatile periods, effectively modeling non-linear market dynamics
  • •Prophet Seasonality: Prophet captured weekly and monthly patterns, improving predictions around earnings seasons and market events
  • •Confidence Intervals: 96% confidence intervals provided reliable uncertainty quantification for risk management

Explore the Live Demo

Experience the interactive forecasting system with real-time predictions, confidence intervals, and detailed model performance metrics.

View Live Demo Back to Portfolio