A Survey on Hyperparameters Optimization of Deep Learning for Time Series Classification

Time series classification (TSC) is essential in various application domains to understand the system dynamics. The adoption of deep learning has advanced TSC, however its performance is sensitive to hyperparameters configuration. Manual tuning of high-dimensional hyperparameters can be labor intensive, leading to a preference for automatic hyperparameters optimization (HPO) methods. To the best of our knowledge, survey papers covering various studies on automatic hyperparameters optimization (HPO) of deep learning for TSC are scarce and even none. To address this gap, we present a systematic literature review to assist researchers in addressing the HPO problem for deep learning in TSC. We analyzed studies published between 2018 and June 2024. This review examines the HPO methods, hyperparameters, and tools utilized in this context based on 77 primary studies sourced from academic databases. The findings indicate that Metaheuristic algorithm and Bayesian Optimization are commonly employed approaches, with a focus on hyperparameters related to the deep learning architectures. This review provides insights that can inform the design and implementation of HPO strategies for deep learning models in time series analysis.