3843273
doi
10.24963/ijcai.2019/932
oai:zenodo.org:3843273
user-eu
Schumann, Anika
IBM Research Zurich
Explainable Deep Neural Networks for Multivariate Time Series Predictions
Assaf, Roy
IBM Research Zurich
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
AI: Machine Learning
AI: Knowledge Representation
Reasoning Applications: Energy
<p>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.</p>
Zenodo
2019-07-01
info:eu-repo/semantics/conferencePaper
3843272
user-eu
award_title=Reliable OM decision tools and strategies for high LCoE reduction on Offshore wind; award_number=745625; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/745625; funder_id=00k4n6c32; funder_name=European Commission;
1590438024.686233
1247477
md5:eb3d3d69332ae51638c10317898a7226
https://zenodo.org/records/3843273/files/0932.pdf
public