Combining Two Machine Learning Models For Enhanced Predictions

Exploring the Benefits of Model Fusion in Machine Learning

Time series forecasting stands as a cornerstone for a wide array of applications. Whether it’s predicting stock prices, demand for a product, or weather patterns, accurate time series forecasting is critical for informed decision-making. Over the years, various modeling techniques have been employed to tackle the intricacies of time series data. Among these methods, two have gained significant popularity: linear regression and XGBoost.

Linear regression is a simple yet robust technique that has been widely used for time series forecasting. On the other hand, XGBoost, an ensemble learning algorithm, has gained acclaim for its exceptional predictive power.

In this article, we delve into the world of time series forecasting by combining the strengths of linear regression and XGBoost. We aim to create a hybrid model that harnesses the simplicity of linear regression and the predictive prowess of XGBoost to provide accurate, robust, and interpretable forecasts for time series data.

Introduction to XGBoost Regression

Imagine you’re trying to solve a complex puzzle. Each piece of the puzzle represents a small part of the…