Data Streams: Investigating Data Structures for Multivariate Asynchronous Time Series Prediction Problems

Christopher Vox, David Broneske, Istiaque Shaikat, Gunter Saake

2023

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.

Paper Citation

in Harvard Style

Vox C., Broneske D., Shaikat I. and Saake G. (2023). Data Streams: Investigating Data Structures for Multivariate Asynchronous Time Series Prediction Problems. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 686-696. DOI: 10.5220/0011737300003411

in Bibtex Style

@conference{icpram23,
author={Christopher Vox and David Broneske and Istiaque Shaikat and Gunter Saake},
title={Data Streams: Investigating Data Structures for Multivariate Asynchronous Time Series Prediction Problems},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={686-696},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011737300003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Data Streams: Investigating Data Structures for Multivariate Asynchronous Time Series Prediction Problems
SN - 978-989-758-626-2
AU - Vox C.
AU - Broneske D.
AU - Shaikat I.
AU - Saake G.
PY - 2023
SP - 686
EP - 696
DO - 10.5220/0011737300003411