Explainable Deep Neural Networks for Multivariate Time Series Predictions
| Autor: | Anika Schumann, Roy Assaf |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Multivariate statistics
AI: Machine Learning Series (mathematics) Computer science business.industry Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences AI: Knowledge Representation 0202 electrical engineering electronic engineering information engineering Deep neural networks 020201 artificial intelligence & image processing Artificial intelligence business Reasoning Applications: Energy 0105 earth and related environmental sciences |
| Zdroj: | IJCAI Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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