计算机科学
插补(统计学)
缺少数据
循环神经网络
人工智能
机器学习
Boosting(机器学习)
时间序列
对抗制
数据挖掘
人工神经网络
作者
Qianli Ma,Sen Li,Garrison W. Cottrell
标识
DOI:10.1109/tpami.2020.3027975
摘要
Incomplete time series classification (ITSC) is an important issue in time series analysis since temporal data often has missing values in practical applications. However, integrating imputation (replacing missing data) and classification within a model often rapidly amplifies the error from imputed values. Reducing this error propagation from imputation to classification remains a challenge. To this end, we propose an adversarial joint-learning recurrent neural network (AJ-RNN) for ITSC, an end-to-end model trained in an adversarial and joint learning manner. We train the system to categorize the time series as well as impute missing values. To alleviate the error introduced by each imputation value, we use an adversarial network to encourage the network to impute realistic missing values by distinguishing real and imputed values. Hence, AJ-RNN can directly perform classification with missing values and greatly reduce the error propagation from imputation to classification, boosting the accuracy. Extensive experiments on 68 synthetic datasets and 4 real-world datasets from the expanded UCR time series archive demonstrate that AJ-RNN achieves state-of-the-art performance. Furthermore, we show that our model can effectively alleviate the accumulating error problem through qualitative and quantitative analysis based on the trajectory of the dynamical system learned by the RNN. We also provide an analysis of the model behavior to verify the effectiveness of our approach.
科研通智能强力驱动
Strongly Powered by AbleSci AI