Lesson 5.3: Forecasting with Tree-Based Models (XGBoost)
Applying powerful ensemble methods like XGBoost to time series forecasting tasks.
The Workflow
The ML Forecasting Workflow
- Data Preparation: Load daily price data for an asset.
- Feature Engineering: Create lagged and rolling window features from the historical price/return data. Define the target variable (e.g., next day's return).
- Train-Test Split: Perform a **chronological walk-forward split** to avoid look-ahead bias.
- Model Training & Tuning: Train and tune an XGBoost model using `TimeSeriesSplit` for cross-validation.
- Evaluation: Compare the model's performance on the unseen test set using appropriate regression or classification metrics.
- Interpretation: Analyze the feature importances to understand what drives the forecast.
Python Implementation
This code snippet demonstrates fitting an XGBoost regressor for a forecasting task.
import pandas as pd
import xgboost as xgb
from sklearn.model_selection import TimeSeriesSplit, GridSearchCV
from sklearn.metrics import mean_squared_error
# Assume X_train, y_train, X_test, y_test are prepared
# with time-series features and a walk-forward split.
# Use TimeSeriesSplit for cross-validation
tscv = TimeSeriesSplit(n_splits=5)
# Define the model and parameter grid for tuning
xgbr = xgb.XGBRegressor(objective='reg:squarederror', random_state=42)
param_grid = {
'n_estimators': [100, 300],
'max_depth': [3, 5],
'learning_rate': [0.01, 0.1]
}
# Perform grid search
grid_search = GridSearchCV(estimator=xgbr, param_grid=param_grid, cv=tscv, scoring='neg_root_mean_squared_error')
grid_search.fit(X_train, y_train)
# Get the best model and evaluate
best_xgb = grid_search.best_estimator_
y_pred = best_xgb.predict(X_test)
rmse = mean_squared_error(y_test, y_pred, squared=False)
print(f"Test RMSE: {rmse:.4f}")
What's Next? Deep Learning for Sequences
Tree-based models are powerful but require manual feature engineering to "see" the past. What if a model could learn the temporal patterns directly from the raw sequence data?
In the next lesson, we will introduce **Deep Learning for Time Series**, exploring how Recurrent Neural Networks (RNNs) and LSTMs use internal memory states to learn from sequential data automatically.