An Experimental Comparison Between Tree-Based and Machine Learning Approaches for Predicting an Oil Temperature in an Electricity Transformer Dataset

Time series data forecasting remains a challenging problem within research communities. A plethora of machine learning and deep learning architectures have been devised to tackle the wide array of time series datasets across different domains. Recent advancements in machine learning algorithms have demonstrated impressive performance. However, there remains a limitation regarding the time-consuming nature of both training and testing phases. Hence, this study endeavors to address whether any methods can achieve comparable performance to recent machine learning algorithms without requiring extensive training time. Through our research, we have identified XGBoost as the optimal solution for addressing this issue. Through comprehensive experiments across three study cases encompassing multivariate to multivariate prediction, multivariate to univariate prediction, and univariate to univariate prediction scenarios, we discovered that XGBoost outperformed other state-of-the-art machine learning techniques in the multivariate-to-multivariate prediction case under ETD dataset.