Existing black box modeling approaches in machine learning suffer from a fixed input and output feature combination. In this paper, a new approach to reconstruct missing variables in a set of time series is presented. An autoencoder is trained as usual with every feature on both sides and the neural network parameters are fixed after this training. Then, the searched variables are defined as missing variables at the autoencoder input and optimized via automatic differentiation. This optimization is performed with respect to the available features loss calculation. With this method, different input and output feature combinations of the trained model can be realized by defining the searched variables as missing variables and reconstructing them. The combination can be changed without training the autoencoder again. The approach is evaluated on the base of a strongly nonlinear electrical component. It is working well for one of four variables missing and generally even for multiple missing variables.

翻译：现有机器学习黑箱建模方法受限于固定的输入与输出特征组合。本文提出一种新颖方法，用于重构时间序列集合中的缺失变量。首先，按照常规方式训练自编码器，使其输入与输出均包含所有特征，训练完成后固定神经网络参数。随后，将待搜索变量定义为自编码器输入端的缺失变量，并通过自动微分进行优化。该优化过程基于可用特征的损失计算展开。通过将待搜索变量定义为缺失变量并对其进行重构，该方法可实现训练模型中不同的输入输出特征组合，且无需重新训练自编码器即可更改组合方式。以强非线性电气元件为基准进行验证，结果表明该方法在单个变量缺失（四变量中缺失其一）时表现良好，在多个变量缺失时通常仍能有效运作。