TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features
Table 1: Effect of masking the data points in Machine Anomaly Detection dataset which are relevant for the explanation.
['Window size', 'Anomalous sequence', 'Flipped prediction after masking', 'Percentage flipped']
In this study, we are trying to infer the causality through the provided explanations, so it is also important to assess the reliability of the generated explanations. We also computed this confidence estimate over the entire test set of Machine Anomaly Detection dataset in order to get an impression regarding the overall reliability of the generated explanations. Since it is important to compute these statistics only over examples where the classification from the network was correct, we were left with 1511 out of 1696 total anomalous sequences in the test set. In the first setup, a masked sequence is generated by suppressing the exact data points for which the explanations have been generated by the textual explanation generator module. Since we are suppressing the exact point, we represent this setup with a window size of one. In the second setup, a sequence is masked with a window size of three covering one preceding and one following value in order to cover up any minor misalignment of the most salient region highlighted by the textual explanation generator module. In this case, a total of three data points were suppressed. We represent this setup with a window size of three. When we relaxed the sanity check criteria to a window size of three, the percentage of flipped sequences rose up to 87.3%. This high success rate makes it evident that in most of the cases, plausible explanations for the predictions made by the network could be provided. However, it is important to note that this experiment does not strongly imply causality.