Benchmarking neural architectures for long-horizon forecasting in ecological simulations
Publication date
2026-05-07
Document type
Konferenzbeitrag
Organisational unit
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2026
First page
111
Last page
122
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Time series forecasting
Convolutional neural networks
Graph neural networks
Ecological modeling
Abstract
Ecological and biological systems exhibit complex nonlinear temporal dynamics that challenge traditional time series forecasting approaches. In this work, we compare multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and graph-based neural architectures for predicting long-term population trajectories in a simulated avian ecosystem. We focus on (i) convolutional temporal filters for capturing seasonal and local dependencies, (ii) assessing whether graph representations of spatial structure improve forecasting performance, and (iii) quantifying robustness under monitoring constraints via data ablations (reduced runs, weekly sampling, reduced scenario diversity). We evaluate model accuracy across multiple forecasting horizons and analyse performance differences across architectures on a large-scale simulation dataset.
Description
This contribution is part of the conference proceedings, which are licensed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)
Version
Published version
Access right on openHSU
Open access