Abstract
We demonstrate that CNN deep neural networks
can not only be used for making predictions based
on multivariate time series data, but also for explaining these predictions. This is important for a
number of applications where predictions are the
basis for decisions and actions. Hence, confidence
in the prediction result is crucial. We design a
two stage convolutional neural network architecture which uses particular kernel sizes. This allows
us to utilise gradient based techniques for generating saliency maps for both the time dimension and
the features. These are then used for explaining
which features during which time interval are responsible for a given prediction, as well as explaining during which time intervals was the joint contribution of all features most important for that prediction. We demonstrate our approach for predicting the average energy production of photovoltaic
power plants and for explaining these predictions