ARIMA_Gold

Forecasting Gold Price using Gradient Boosting Machines (XGBOOST), Decision Trees and Random Forests and ARIMA

Steps for Forecasting Gold Price

Step 1: Data Analysis

1. Data Collection: Gather historical gold prices and relevant external factors.
2. Preprocessing: Handle missing values, convert dates, and engineer features (e.g., lags, rolling statistics).
3. EDA: Visualize trends, seasonality, and correlations. Test for stationarity.
4. Data Split: Divide into train-test sets, ensuring temporal order.

Step 2: Decision Trees & Random Forests

1. Decision Trees:

   • Train a Decision Tree Regressor.
   • Tune parameters (e.g., depth).
   • Evaluate using MAE, RMSE, feature importance.

2. Random Forests:

   • Train a Random Forest Regressor.
   • Optimize parameters (e.g., number of trees).
   • Assess performance and feature importance.

Step 3: Gradient Boosting Machines (XGBoost)

1. Feature Engineering: Enhance features with interaction terms, advanced rolling stats.
2. Model Training:
   • Train XGBoost with hyperparameter tuning (e.g., learning rate, depth).
   • Use cross-validation.
   • Evaluate using MAE, RMSE, and SHAP values for feature importance.

Step 4: ARIMA

1. Preprocessing: Ensure stationarity, possibly by differencing.
2. Model Training:
   • Fit ARIMA using identified lags (p, d, q).
   • Diagnose residuals for model fit.
   • Evaluate with MAE, RMSE, AIC/BIC.

Step 5: Rating Models

1. Compare Models: Rank based on MAE, RMSE, and complexity.
2. Ensemble Option: Consider combining models for improved accuracy.
3. Final Selection: Choose the best model for deployment, based on performance and practicality.
4. Reporting: Summarize results with visualizations and key findings.