Towards Learning to Rank Deep-Learning Models for Multivariate Time-Series Transfer Learning

Selecting an unsuitable deep-learning (DL) model for often results in negative transfer and poor performance. To address this, we propose a novel ranking approach designed to guide DL model selection by leveraging dataset characteristics in multivariate time-series data. Our approach considers three dataset characteristics: statistical, shape-based, and a combination of both. By implicitly learning dataset similarities, the ranking model identifies the most suitable DL model for positive transfer learning. Experiments show that a ranking model effectively learns to rank the most suitable DL model at position one. The impact of characteristics on the ranking model's performance depends on the size of the training rankings. This approach highlights the importance of learning to select DL models for on multivariate time-series datasets and offers a practical solution for effective in time-series applications.