Summary of Integrating Marketing Channels Into Quantile Transformation and Bayesian Optimization Of Ensemble Kernels For Sales Prediction with Gaussian Process Models, by Shahin Mirshekari et al.
Integrating Marketing Channels into Quantile Transformation and Bayesian Optimization of Ensemble Kernels for Sales Prediction with Gaussian Process Models
by Shahin Mirshekari, Negin Hayeri Motedayen, Mohammad Ensaf
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study proposes a novel Gaussian Process (GP) model that combines Radial Basis Function (RBF), Rational Quadratic, and Matérn kernels using an ensemble kernel. This integration is optimized through Bayesian optimization to find the optimal weights for each kernel, allowing the model to better capture complex sales data patterns. The proposed approach outperforms traditional GP models, achieving a notable 98% accuracy and superior performance across metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R^2). This advancement highlights the effectiveness of ensemble kernels and Bayesian optimization in improving predictive accuracy, with implications for machine learning applications in sales forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to forecast product sales using a special type of math called Gaussian Process. They combine three different types of functions (Radial Basis Function, Rational Quadratic, and Matérn) to make the model more accurate. To find the right combination of these functions, they use a technique called Bayesian optimization. This approach works really well, achieving an accuracy rate of 98%! It also performs better than other models in terms of how close it gets to actual sales figures. |
Keywords
» Artificial intelligence » Machine learning » Mae » Mse » Optimization
