An Optimised Ensemble Approach for Multivariate Multi-Step Forecasts Using the Example of Flood Levels

Michel Spils, Sven Tomforde

2024

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.

Paper Citation

in Harvard Style

Spils M. and Tomforde 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, SciTePress, pages 388-396. DOI: 10.5220/0012396000003636

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Spils M.
AU - Tomforde S.
PY - 2024
SP - 388
EP - 396
DO - 10.5220/0012396000003636
PB - SciTePress