2025
3.
Albrecht, Steffen; Kim, Alex; Madelino, João; Dost, Katharina; Zhu, Johnny; Broderick, David; Poonawala-Lohani, Nooriyan; Jamison, Sarah; Rasanathan, Damayanthi; Stanley, Alicia; Lawrence, Shirley; Marsh, Samantha; Castelino, Lorraine; Trenholme, Adrian; Turner, Nikki; McIntyre, Peter; Paynter, Janine; Riddle, Pat; Grant, Cameron; Wicker, Jörg; Dobbie, Gillian
How does a GPT perform in Forecasting Severe Respiratory Disease Hospitalizations? Proceedings Article
In: 31th International Conference on Neural Information Processing (ICONIP) , Auckland University of Technology (AUT) Library, 2025.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: influenza, machine learning, time series, time series forecasting
@inproceedings{nokey,
title = {How does a GPT perform in Forecasting Severe Respiratory Disease Hospitalizations?},
author = {Steffen Albrecht and Alex Kim and Jo\~{a}o Madelino and Katharina Dost and Johnny Zhu and David Broderick and Nooriyan Poonawala-Lohani and Sarah Jamison and Damayanthi Rasanathan and Alicia Stanley and Shirley Lawrence and Samantha Marsh and Lorraine Castelino and Adrian Trenholme and Nikki Turner and Peter McIntyre and Janine Paynter and Pat Riddle and Cameron Grant and J\"{o}rg Wicker and Gillian Dobbie},
doi = { 10.24135/iconip1},
year = {2025},
date = {2025-03-17},
booktitle = {31th International Conference on Neural Information Processing (ICONIP) },
volume = {1},
publisher = {Auckland University of Technology (AUT) Library},
abstract = {Forecasting surges in hospital admissions caused by severe respiratory infections is of crucial importance during the winter season to enable proactive hospital management and timely decision-making to prevent healthcare system overload. As time series derived from hospital surveillance systems for these severe cases are sparse and encode weak seasonality patterns, machine learning is key to computing accurate forecasts. The most recent algorithmic advance in time series forecasting is the adaptation of generative pre-trained transformers (GPTs). Those models, pre-trained on large datasets, have the potential to transfer knowledge to smaller datasets, such as hospital surveillance data, for very specific and well-defined case definitions. We demonstrate that despite this great potential for such practical applications, one of the largest first-generation GPTs is not able to provide accurate forecasts and is outperformed by simple linear models and even na\"{i}ve forecasts. DOI: https://doi.org/10.24135/ICONIP1
},
keywords = {influenza, machine learning, time series, time series forecasting},
pubstate = {published},
tppubtype = {inproceedings}
}
Forecasting surges in hospital admissions caused by severe respiratory infections is of crucial importance during the winter season to enable proactive hospital management and timely decision-making to prevent healthcare system overload. As time series derived from hospital surveillance systems for these severe cases are sparse and encode weak seasonality patterns, machine learning is key to computing accurate forecasts. The most recent algorithmic advance in time series forecasting is the adaptation of generative pre-trained transformers (GPTs). Those models, pre-trained on large datasets, have the potential to transfer knowledge to smaller datasets, such as hospital surveillance data, for very specific and well-defined case definitions. We demonstrate that despite this great potential for such practical applications, one of the largest first-generation GPTs is not able to provide accurate forecasts and is outperformed by simple linear models and even naïve forecasts. DOI: https://doi.org/10.24135/ICONIP1
2024
2.
Albrecht, Steffen; Broderick, David; Dost, Katharina; Cheung, Isabella; Nghiem, Nhung; Wu, Milton; Zhu, Johnny; Poonawala-Lohani, Nooriyan; Jamison, Sarah; Rasanathan, Damayanthi; Huang, Sue; Trenholme, Adrian; Stanley, Alicia; Lawrence, Shirley; Marsh, Samantha; Castelino, Lorraine; Paynter, Janine; Turner, Nikki; McIntyre, Peter; Riddle, Pat; Grant, Cameron; Dobbie, Gillian; Wicker, Jörg
Forecasting severe respiratory disease hospitalizations using machine learning algorithms Journal Article
In: BMC Medical Informatics and Decision Making, vol. 24, iss. 1, pp. 293, 2024, ISSN: 1472-6947.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: influenza, machine learning, time series, time series forecasting
@article{Albrecht2024forecasting,
title = {Forecasting severe respiratory disease hospitalizations using machine learning algorithms},
author = {Steffen Albrecht and David Broderick and Katharina Dost and Isabella Cheung and Nhung Nghiem and Milton Wu and Johnny Zhu and Nooriyan Poonawala-Lohani and Sarah Jamison and Damayanthi Rasanathan and Sue Huang and Adrian Trenholme and Alicia Stanley and Shirley Lawrence and Samantha Marsh and Lorraine Castelino and Janine Paynter and Nikki Turner and Peter McIntyre and Pat Riddle and Cameron Grant and Gillian Dobbie and J\"{o}rg Wicker},
url = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02702-0},
doi = {10.1186/s12911-024-02702-0},
issn = {1472-6947},
year = {2024},
date = {2024-10-09},
urldate = {2024-10-07},
journal = {BMC Medical Informatics and Decision Making},
volume = {24},
issue = {1},
pages = {293},
abstract = {Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses.},
keywords = {influenza, machine learning, time series, time series forecasting},
pubstate = {published},
tppubtype = {article}
}
Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses.
2023
1.
Chen, Zeyu; Dost, Katharina; Zhu, Xuan; Chang, Xinglong; Dobbie, Gillian; Wicker, Jörg
Targeted Attacks on Time Series Forecasting Proceedings Article
In: Kashima, Hisashi; Ide, Tsuyoshi; Peng, Wen-Chih (Ed.): The 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 314-327, Springer Nature Switzerland, Cham, 2023, ISSN: 978-3-031-33383-5.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: adversarial learning, forecasting, machine learning, time series
@inproceedings{Chen2023targeted,
title = {Targeted Attacks on Time Series Forecasting},
author = {Zeyu Chen and Katharina Dost and Xuan Zhu and Xinglong Chang and Gillian Dobbie and J\"{o}rg Wicker},
editor = {Hisashi Kashima and Tsuyoshi Ide and Wen-Chih Peng},
url = {https://github.com/wickerlab/nvita},
doi = {10.1007/978-3-031-33383-5_25},
issn = {978-3-031-33383-5},
year = {2023},
date = {2023-05-26},
urldate = {2023-05-26},
booktitle = {The 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
pages = {314-327},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Abstract. Time Series Forecasting (TSF) is well established in domains dealing with temporal data to predict future events yielding the basis for strategic decision-making. Previous research indicated that forecasting models are vulnerable to adversarial attacks, that is, maliciously crafted perturbations of the original data with the goal of altering the model’s predictions. However, attackers targeting specific outcomes pose a substantially more severe threat as they could manipulate the model and bend it to their needs. Regardless, there is no systematic approach for targeted adversarial learning in the TSF domain yet. In this paper, we introduce targeted attacks on TSF in a systematic manner. We establish a new experimental design standard regarding attack goals and perturbation control for targeted adversarial learning on TSF. For this purpose, we present a novel indirect sparse black-box evasion attack on TSF, nVita. Additionally, we adapt the popular white-box attacks Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). Our experiments confirm not only that all three methods are effective but also that current state-of-the-art TSF models are indeed susceptible to attacks. These results motivate future research in this area to achieve higher reliability of forecasting models.},
keywords = {adversarial learning, forecasting, machine learning, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
Abstract. Time Series Forecasting (TSF) is well established in domains dealing with temporal data to predict future events yielding the basis for strategic decision-making. Previous research indicated that forecasting models are vulnerable to adversarial attacks, that is, maliciously crafted perturbations of the original data with the goal of altering the model’s predictions. However, attackers targeting specific outcomes pose a substantially more severe threat as they could manipulate the model and bend it to their needs. Regardless, there is no systematic approach for targeted adversarial learning in the TSF domain yet. In this paper, we introduce targeted attacks on TSF in a systematic manner. We establish a new experimental design standard regarding attack goals and perturbation control for targeted adversarial learning on TSF. For this purpose, we present a novel indirect sparse black-box evasion attack on TSF, nVita. Additionally, we adapt the popular white-box attacks Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). Our experiments confirm not only that all three methods are effective but also that current state-of-the-art TSF models are indeed susceptible to attacks. These results motivate future research in this area to achieve higher reliability of forecasting models.