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Authors: Michel Spils and Sven Tomforde

Affiliation: Department of Computer Science, University of Kiel, Kiel, Germany

Keyword(s): Machine Learning, Time-Series Forecasting, Ensemble Deep Learning, Flood Forecasting.

Abstract: Deep Learning methods have become increasingly popular for time-series forecasting in recent years. One common way of improving time-series forecasts is to use ensembles. By combining forecasts of different models, for example calculating the mean forecast, it is possible to get an ensemble that performs better than each single member. This paper suggests a method of aggregating ensemble forecasts using another neural network.The focus is on multivariate multi-step ahead forecasting. Experiments are done on 5 water levels at small to medium-sized rivers and show improvements on naive ensembles and single neural networks.

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Paper citation in several formats:
Spils, M., Tomforde and S. (2024). An Optimised Ensemble Approach for Multivariate Multi-Step Forecasts Using the Example of Flood Levels. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 388-396. DOI: 10.5220/0012396000003636

@conference{icaart24,
author={Michel Spils and Sven Tomforde},
title={An Optimised Ensemble Approach for Multivariate Multi-Step Forecasts Using the Example of Flood Levels},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={388-396},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012396000003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - An Optimised Ensemble Approach for Multivariate Multi-Step Forecasts Using the Example of Flood Levels
SN - 978-989-758-680-4
IS - 2184-433X
AU - Spils, M.
AU - Tomforde, S.
PY - 2024
SP - 388
EP - 396
DO - 10.5220/0012396000003636
PB - SciTePress

- Science and Technology Publications, Lda.
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