Interactive visualization generation method for time series data based on transfer learning
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.
interactive visualization generationtransfer learningtime series data visual analysispattern re-commendation
万方数据
维普
