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We propose an adversarial learning method to tackle a Domain Adaptation (DA)time series regression task (DANNTe). The regression aims at building a virtualcopy of a sensor installed on a gas turbine, to be used in place of thephysical sensor which can be missing in certain situations. Our DA approach isto search for a domain-invariant representation of the features. The learnerhas access to both a labelled source dataset and an unlabeled target dataset(unsupervised DA) and is trained on both, exploiting the minmax game between atask regressor and a domain classifier Neural Networks. Both models share thesame feature representation, learnt by a feature extractor. This work is basedon the results published by Ganin et al. arXiv:1505.07818; indeed, we presentan extension suitable to time series applications. We report a significantimprovement in regression performance, compared to the baseline model trainedon the source domain only.

DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift