Ensemble-based versus expert-assisted approach to carbon price features selection

Abstract

The paper comments on two main issues. First, on a model for estimating the carbon price using multi-year market data. And second, on the consideration of two approaches to feature set exploitation. On the one hand, two ensemble machine-learning models with randomly selected feature sets are employed. On the other hand, a hybrid feature selection strategy follows domain expertise on which features should be explored. This minimizes the number of feature set combinations to be tested. The additional information for the predictions was the data from other commodity contracts, which could be easily introduced into the collection, as too many of them do not necessarily improve the estimates. The results of the experiments are promising: for the model based on SVR, the MAPE obtained was 2.09% and 5.6% for the following day and week price forecasts, respectively.

Towards Algorithm-Assisted Career Management - a Challenge for New Immigration Countries. Predicting Migrants' Work Trajectory Using Ensemble Learning

“…It allows for the delivery of well-performing models from candidates that initially appear weak. However, as it has been reported for many fields, for instance, precision agriculture (Ruszczak et al, 2020) or marketing research (Ruszczak et al, 2023), it supports the pattern recognition finding and confirmation (Nalepa et al, 2021). The ensemble machine learning techniques could also significantly aid the reasoning and help find new internal data relations for labour studies.…”