Machine Learning for Multiple Domains

Abstract

Machine Learning has many tools, techniques, and algorithms for data science. In this project, machine learning is applied to the Wind and Solar Power Forecasting and Foreign Exchange Markets. We use feature engineering to find correlations between the two domains. These are then applied to representative data, which is used with classification schemes, such as linear regression, and artificial neural networks. The focus is Wind and Solar power prediction in the context of international economics. The research goal is to find the best combination of features and classifier for an interdisciplinary model.