基于迁移学习的交互时序数据可视化生成方法

Interactive visualization generation method for time series data based on transfer learning

为了解决时序数据间分布不一致的问题,使模式分析更容易应用于其他数据,提出基于迁移学习的交互式时序数据可视化生成方法,将迁移成分分析应用于时序数据提取的特征.将用户在其中一个时序数据上的分析作为标签,在该源域上训练分类器并应用到多个目标域,对多个不同目标域的时序数据的模式进行批量推荐.通过真实的天气数据和轴承信号数据的2个案例分析和专家访谈,验证了利用该方法能够提高时序数据探索的效率,减少数据分布不一致问题带来的影响,体现该方法的有效性和实用性.

An interactive visualization generation method for time series data based on transfer learning was proposed in order to address the inconsistency in data distribution across time-series data and facilitate the application of pattern analysis to other data.Transfer component analysis was applied to transfer features extracted from each time series data.The user’s analysis on one of the time series data served as labels.The classifier was trained on the source domain and applied to multiple target domains in order to achieve pattern recommendations.Two case studies and expert interviews with real-world weather data and bearing signal data were conducted to verify the effectiveness and practicality of the method by improving the efficiency of temporal data exploration and reducing the impact of inconsistent data distribution.