HIVE-COTE is a hierarchical method combining different classifiers into ensembles to increase the performance of the classification capabilities. It combines 37 different classifiers that use all the above-mentioned techniques, including frequency, and shapelet transformation domains. HIVE-COTE is currently the classifier that performs best on the UCR-datasets [12], and it is considered state of the art in TSC. The major drawback with both COTE and DTW methods is the high computational cost. Lately, the hegemony of COTE and DTW has been challenged by several efforts of using artificial neural networks for TSC [22]. For example [PERSON] et al. [23] applied Multi-Channel Deep Convolutional Neural Networks (MC-CNN) on data for human activity as well as on congestive hearing failure data. They found that MC-CNN is more efficient, and competitive in accuracy, compared to state-of-the-art traditional TSC algorithms (1-NN DTW). A fully Convolutional Neural Network (CNN), deep multilayer perceptions network (Dense Neural Network, DNN) and a deep Residual Neural Network architecture was tested in a univariate TSC setting in [24]. They found that both their fully connected Convolutional Neural Network and the deep Residual Neural Network architectures achieve better performance compared to other state-of-the-art approaches. Both Recurrent Neural Networks (RNN) [25] and Convolutional Neural Networks (CNN) for TSC are showing state-of-the-art performance, outperforming other techniques on some datasets, but not on others [22; 23; 24]. Due to their nature, RNNs are a natural choice when dealing with time series; however, one of their drawbacks is that they use more time on optimization. In a review of TSC methods, [PERSON] et al. [PERSON] et al. (2019) found that CNNs outperform RNNs not only in training time, but also in accuracy. An important issue that many TSC techniques cannot achieve is to quantify prediction uncertainty. One way to overcome this limitation, and retain state-of-the-art predictive power, is to use Bayesian Neural Networks [PERSON] et al. (2016), e.g., Bayesian Convolutional Neural Networks (BCNNs) or Bayesian Recurrent Neural Networks (BRNNs). These have the same advantages as the standard neural networks, but have the have the additional benefit of providing posterior predictive distributions. When dealing with a binary classification problem, such as the leak vs. no-leak classification used here, the output from a Bayesian Neural Network is a probability estimate, or level of uncertainty, of the class that a given time series belongs to. This uncertainty is important information when making decisions based on the classification, such as to mobilize a costly confirmation and localization survey [PERSON] et al. (2016). The major drawback with classical Bayesian Neural Networks is their failure to up-scale to large data sets. [PERSON] and [PERSON] [PERSON] and [PERSON] (2017) have recently proposed to use Bernoulli dropout during both the training and the testing stages. This can be viewed as an approximate variational inference technique [PERSON] et al. (2018). [PERSON] et al. [PERSON] et al. (2018) presented an algorithm called _Bayes by backprop_ and showed that it can efficiently estimate the distributions over the model parameters. [PERSON] et al. [PERSON] et al. (2018) applied the Bayes by backprop algorithm in different CNNs and for different data sets, and compared it with the [PERSON] and [PERSON] approach. They found that the two approaches are comparable. The simple and applicable nature of the method by [PERSON] and [PERSON] has made it popular in a wide range of applications where quantification of uncertainty is important, e.g., [PERSON] and [PERSON] (2017); [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2019). In their review of the status and future of deep-learning approaches in oceanography, [PERSON] et al. [PERSON] et al. (2018) argues that deep learning is still an infant method within marine science.