Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions

Time series analysis has been widely employed in various domains, including finance, healthcare, meteorology, and economics. This approach is crucial in extracting patterns, discerning trends, and forecasting future data points. Traditional approaches for time series analysis often struggle to capture the complex relationships and dependencies present in real-world time series data. Recently, deep learning has shown remarkable performance on many time series analysis tasks. Numerous deep-learning architectures have been proposed to cater the diversity of time-series datasets across different domains. Although advanced deep learning models have considerable computational capabilities, they may fail to detect complex correlations that exist within certain datasets. The performance of particular deep learning models could be impacted by factors such as data noise, model design, and hyperparameter setups. Modern time series data, with its challenges and non-linearities, requires even more refined techniques. In this context, ensemble deep learning techniques have emerged as a promising solution. By combining multiple models, ensemble learning addresses the limitations of individual models, enhancing prediction accuracy and robustness. Despite significant advancements made in this domain, our literature review reveals a critical gap. The study aims to bridge this gap by providing a pioneering investigation into the application of ensemble techniques to time series analysis. We systematically categorize existing ensemble methods used in diverse domains and conduct a comprehensive literature review to understand the current state of ensemble deep learning in time series analysis. Our research explores the complexities of the three main ensemble techniques—bagging, boosting, and stacking—and extends to include adaptive and hybrid ensemble methods, which are increasingly relevant for time series forecasting. We provide a conceptual framework and pseudocode for practical implementation, allowing for a thorough examination of these established and emerging techniques. Additionally, our review discusses the evaluation metrics commonly used in ensemble learning for time series analysis, including statistical tests that assess the significance of the improvements offered by ensemble methods. Moreover, we analyze and examine the primary challenges, unresolved issues, and possible directions for future research in this rapidly evolving field. This study is a significant and original contribution to the domain, setting the foundation for future research and progress in combining ensemble deep learning with time series analysis.