论文标题
复杂的顺序数据分析：现有算法的系统文献综述
Complex Sequential Data Analysis: A Systematic Literature Review of Existing Algorithms
论文作者
论文摘要
本文综述了对使用深度学习框架进行分析离散不规则的复杂顺序数据集的方法。这种数据集的典型示例是财务数据，其中特定事件触发数据序列的突然不规则变化。在试图分析这些数据集时，传统的深度学习方法的性能很差甚至失败。系统文献综述的结果揭示了基于复发性神经网络的框架的主导地位。发现深度学习框架的性能主要使用平均绝对误差和均方根误差精度指标进行评估。所确定的根本挑战是：缺乏性能鲁棒性，方法论的非透明度，内部和外部建筑设计和配置问题。这些挑战为改善复杂不规则的顺序数据集的框架提供了机会。
This paper provides a review of past approaches to the use of deep-learning frameworks for the analysis of discrete irregular-patterned complex sequential datasets. A typical example of such a dataset is financial data where specific events trigger sudden irregular changes in the sequence of the data. Traditional deep-learning methods perform poorly or even fail when trying to analyse these datasets. The results of a systematic literature review reveal the dominance of frameworks based on recurrent neural networks. The performance of deep-learning frameworks was found to be evaluated mainly using mean absolute error and root mean square error accuracy metrics. Underlying challenges that were identified are: lack of performance robustness, non-transparency of the methodology, internal and external architectural design and configuration issues. These challenges provide an opportunity to improve the framework for complex irregular-patterned sequential datasets.
