Authors:
Christopher Vox
1
;
David Broneske
2
;
Istiaque Shaikat
1
and
Gunter Saake
3
Affiliations:
1
Volkswagen AG, Wolfsburg, Germany
;
2
German Centre for Higher Education Research and Science Studies, Hannover, Germany
;
3
Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany
Keyword(s):
Multivariate Asynchronous Time Series, Deep Learning, Convolutional Neural Network, Recurrent Neural Network.
Abstract:
Time series data are used in many practical applications, such as in the area of weather forecasting or in the automotive industry to predict the aging of a vehicle component. For these practical applications, multivariate time series are present, which are synchronous or asynchronous. Asynchronicity can be caused by different record frequencies of sensors and often causes challenges to the efficient processing of data in data analytics tasks. In the area of deep learning, several methods are used to preprocess the data for the respective models appropriately. Sometimes these data preprocessing methods result in a change of data distribution and thus, to an introduction of data based bias. Therefore, we review different data structures for deep learning with multivariate, asynchronous time series and we introduce a lightweight data structure which utilizes the idea of stacking asynchronous data for deep learning problems. As data structure we create the Triplet-Stream with decreased
memory footprint, which we evaluate for one classification problem and one regression problem. The Triplet-Stream enables excellent performance on all datasets compared to current approaches.
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