A Competitive Scenario Forecaster using XGBoost and Gaussian Copula
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
In recent years scenario forecasting has been explored and developed by multiple authors.
It is a useful technique for setting such as renewable energy production, which
is extremely important for a society transitioning from fossil fuel energy generation.
Currently, one of the methods to approach the task of scenario forecasting are generative
models. The primary goal of this thesis is to develop an approach that outperforms the
current best model, using the decision tree model method. This work also discusses
possible improvements for decision tree models in scenario forecasting setting. Our
approach has surpassed the performance of generative models, making it a solid new
baseline for future researchers to beat.
Description
Keywords
XGBoost, Gaussian Copula, Quantile forecasting, Scenario forecasting, Energy forecasting, Time series