针对因入境时间短、组帧错误等原因导致的卫星帆板温度遥测数据缺失问题，提出一种基于引入注意力机制的时间卷积网络（SE-TCN）的自回归预测方法。温度遥测数据可看作是具有较强规律性的渐周期信号，采用SE-TCN对历史数据到未来数据的映射进行拟合完成缺失值的插补，同时为表征对实际缺失数据集的插补效果，增加评价指标的计算方式，有效解决了使用物理模型仿真和统计学方法插值偏差过大，及无法计算实际插值效果的问题。与长短时记忆网络和时间卷积网络等模型相比，SE-TCN在测试集和实际缺失数据集上均得到了更好的插值效果。
This paper proposes an autoregressive prediction method based on time convolutional network with attentional mechanism(time convolution network with squeeze and excitation, SE-TCN), to solve the problem of missing telemetry data of satellite panel temperature due to short entry time, framing error, and other reasons. Temperature telemetry data is considered to be a strong regularity of periodic signal, so this paper adopts the SE-TCN model to map from historical data to the data in the future, which completes the missing value interpolation and effectively solves the problem that the interpolation deviation of the physical model and statistical method is too large. At the same time, in order to characterize the interpolation effect on the actual missing data set, the calculation method of the evaluation index is added in this paper. Compared with long short-term memory network and time convolutiion network models, SE-TCN has a better interpolation effect on both the test set and the actual missing data set.
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