How can I optimize hyperparameters in a lightGBM model for time-series forecasting

Question

With the help of proper code example can you tell me How can I optimize hyperparameters in a lightGBM model for time-series forecasting?

Answer 1

Optimize LightGBM hyperparameters for time-series forecasting using Bayesian Optimization (Optuna) or Grid Search (Scikit-learn) while ensuring time-aware cross-validation (e.g., TimeSeriesSplit).

Here is the code snippet you can refer to:

In the above code we are using the following key approaches:

• Time-Series Aware Cross-Validation:

  • Uses TimeSeriesSplit to prevent data leakage and ensure correct evaluation.

• Optuna for Hyperparameter Optimization:

  • Optimizes learning rate, num_leaves, max_depth, subsample, colsample_bytree, etc.
  • Uses Bayesian Optimization for faster and efficient hyperparameter tuning.

• Early Stopping for Regularization:

  • Prevents overfitting by stopping training when validation loss stops improving.

• Mean Absolute Error (MAE) as Evaluation Metric:

  • Ensures performance is assessed accurately for time-series forecasting.

Hence, using Optuna with TimeSeriesSplit in LightGBM enables efficient hyperparameter optimization, ensuring better forecasting accuracy while avoiding overfitting.