Random forests in time series analysis

Machine learning algorithms are becoming more relevant in many fields from neuroscience to biostatistics, due to their adaptability and the possibility to learn from the data. In recent years, those techniques became popular in economics and found different applications in policymaking, financial forecasting, and portfolio optimization. The aim of this dissertation is two-fold. First, I will provide a review of the classification and Regression Tree and Random Forest methods proposed by [Breiman, 1984], [Breiman, 2001], then I study the effectiveness of those algorithms in time series analysis. I review the CART model and the Random Forest, which is an ensemble machine learning algorithm, based on the CART, using a variety of applications to test the performance of the algorithms. Second, I will implement an application on financial data: I will use the Random Forest algorithm to estimate a factor model based on macroeconomic variables with the aim of verifying if the Random Forest is able to capture part of the non-linear relationship between the factor considered and the index return.