About this Research Topic
In recent years, the application of deep learning techniques for time series prediction has surged, owing to their capacity to grasp intricate temporal patterns. However, their efficacy across various real-world scenarios remains unclear. Financial data, climate data, healthcare data, and industrial sensor data pose unique challenges, necessitating a thorough investigation into the adaptability and performance of deep learning models.
This Research Topic aims to explore the effectiveness of deep learning approaches in predicting time series data across diverse real-world scenarios. We seek to address the following questions:
• How do different deep learning architectures perform on various types of time series data? What are the challenges associated with applying these techniques in real-world settings?
• How can we enhance the interpretability of deep learning models for time series prediction tasks?
Contributors are invited to explore the applicability of deep learning models (such as RNNs, and CNNs) on different types of time series data, including financial, climate, healthcare, and industrial sensor data. We encourage submissions that address challenges such as data scarcity, noise, irregular sampling, and concept drift. Additionally, we seek manuscripts that integrate eXplainable AI (XAI) techniques to enhance model interpretability.
Manuscript types include original research articles, reviews, and methodological papers.
This Research Topic aims to explore the effectiveness of deep learning approaches in predicting time series data across diverse real-world scenarios. We seek to address the following questions:
• How do different deep learning architectures perform on various types of time series data? What are the challenges associated with applying these techniques in real-world settings?
• How can we enhance the interpretability of deep learning models for time series prediction tasks?
Contributors are invited to explore the applicability of deep learning models (such as RNNs, and CNNs) on different types of time series data, including financial, climate, healthcare, and industrial sensor data. We encourage submissions that address challenges such as data scarcity, noise, irregular sampling, and concept drift. Additionally, we seek manuscripts that integrate eXplainable AI (XAI) techniques to enhance model interpretability.
Manuscript types include original research articles, reviews, and methodological papers.
Keywords: Deep Learning, XAI, Time series analysis, Big Data Technology, Interpretation, Machine Learning Models
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
