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Publication . Conference object . 2019

Explainable Deep Neural Networks for Multivariate Time Series Predictions

Roy Assaf; Anika Schumann;
Open Access
Published: 01 Jul 2019
Publisher: International Joint Conferences on Artificial Intelligence Organization
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.

Subjects by Vocabulary

Microsoft Academic Graph classification: Multivariate statistics Series (mathematics) Computer science Deep neural networks Artificial intelligence business.industry business Pattern recognition

Subjects

AI: Machine Learning, AI: Knowledge Representation, Reasoning Applications: Energy

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EC| ROMEO
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ROMEO
Reliable OM decision tools and strategies for high LCoE reduction on Offshore wind
  • Funder: European Commission (EC)
  • Project Code: 745625
  • Funding stream: H2020 | IA
Validated by funder
,
EC| ROMEO
Project
ROMEO
Reliable OM decision tools and strategies for high LCoE reduction on Offshore wind
  • Funder: European Commission (EC)
  • Project Code: 745625
  • Funding stream: H2020 | IA
Validated by funder
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