Time Series Capstone in AI & Artificial Intelligence

Real-world financial data is complex. To capture every pattern, you will build a Hybrid Ensemble. You'll use XGBoost to process technical indicators (like RSI and MACD) and categorical features (day of week). Simultaneously, you'll use an LSTM to process the raw price sequence to capture long-term momentum. By combining their predictions, you create a model that is significantly more robust than any single architecture.

A stock model that can't survive a backtest is a liability. You will implement a rigorous Walk-Forward Validation scheme across three years of historical data. You will ensure that at no point does your model 'see' future prices. You'll calculate not just error (RMSE), but also Directional Accuracy—how often your model correctly predicts if the price will go up or down, regardless of the magnitude.

To graduate, you must demonstrate that your model is usable. You will build a simulated trading strategy based on your forecasts and calculate its Sharpe Ratio and Max Drawdown. This level of professional evaluation is what separates an AI researcher from a quantitative developer. You will prove that your temporal intelligence can provide consistent, risk-managed value in a volatile world.

Welcome to the Time Series Capstone. You've mastered statistics, machine learning, and deep learning for time. Now, you will build a complete, professional stock price prediction pipeline.

Your mission is to predict the 'Next 7 Days' of a high-volatility stock. You'll use data engineering, ensemble modeling, and rigorous backtesting to prove your results.

You'll start by engineering technical indicators like RSI and Moving Averages. Then, you'll train your models and evaluate them using RMSE and MAPE.

Capstone Check: Why use an 'Ensemble' of XGBoost and LSTM for this project?

• →To have more lines of code
• →Because they capture different things: XGBoost handles tabular features/indicators, and LSTM captures sequential memory

Finally, you'll perform a Sharpe Ratio analysis to see if your predictions actually translate into a viable trading strategy. It's not just about math; it's about results.

Once complete, you will have a world-class Time Series portfolio project. You are now a master of the temporal dimension. Congratulations!

Final Check: What is the most critical rule of Time Series analysis to remember for your career?

• →Write fast code
• →Never shuffle your data or leak future information into your training set

Time Series track complete! You've mastered the art and science of the timeline. Go forth and predict!

Score a Real Forecast. Finish computing the Mean Absolute Error between actual and predicted values.