Gradient Boosting: a Silver Bullet in Forecasting

<p>Time-series forecasting is a crucial task in many domains, including finance, sales, and weather prediction. While classical timeseries models and deep learning techniques have been widely used for this purpose, there&rsquo;s growing evidence that gradient boosting often outshines other methods.</p> <h1>What is gradient boosting?</h1> <p><a href="https://en.wikipedia.org/wiki/Gradient_boosting" rel="noopener ugc nofollow" target="_blank">Gradient boosting</a>&nbsp;is a machine learning technique that builds predictive models by combining an ensemble of weak learners in a sequential manner. It aims to create a strong learner by iteratively minimizing the errors made by the previous models. The core idea is to fit subsequent models to the residuals of the previous models, gradually improving predictions with each iteration.</p> <p><a href="https://medium.com/towards-data-science/gradient-boosting-a-silver-bullet-in-forecasting-5820ba7182fd"><strong>Read More</strong></a></p>