2022
2.
Poonawala-Lohani, Nooriyan; Riddle, Pat; Adnan, Mehnaz; Wicker, Jörg
Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains: Ensemble methods for spatio-temporal Time Series Forecasting of Influenza-like Illness Proceedings Article
In: pp. 1-7, Association for Computing Machinery, New York, NY, USA, 2022, ISBN: 9781450393867.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: bioinformatics, computational sustainability, dynamic time warping, forecasting, influenza, machine learning, medicine, time series
@inproceedings{Poonawala-Lohani2022geographic,
title = {Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains: Ensemble methods for spatio-temporal Time Series Forecasting of Influenza-like Illness},
author = {Nooriyan Poonawala-Lohani and Pat Riddle and Mehnaz Adnan and J\"{o}rg Wicker},
doi = {10.1145/3535508.3545562},
isbn = {9781450393867},
year = {2022},
date = {2022-08-07},
pages = {1-7},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Influenza is a communicable respiratory illness that can cause serious public health hazards. Flu surveillance in New Zealand tracks case counts from various District health boards (DHBs) in the country to monitor the spread of influenza in different geographic locations. Many factors contribute to the spread of the influenza across a geographic region, and it can be challenging to forecast cases in one region without taking into account case numbers in another region. This paper proposes a novel ensemble method called Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains (GEO-Reach). GEO-Reach is an ensemble technique that uses a two layer approach to utilise interdependence of historical case counts between geographic regions in New Zealand. This work extends a previously published method by the authors called Randomized Ensembles of Auto-regression chains (Reach). State-of-the-art forecasting models look at studying the spread of the virus. They focus on accurate forecasting of cases for a location using historical case counts for the same location and other data sources based on human behaviour such as movement of people across cities/geographic regions. This new approach is evaluated using Influenza like illness (ILI) case counts in 7 major regions in New Zealand from the years 2015-2019 and compares its performance with other standard methods such as Dante, ARIMA, Autoregression and Random Forests. The results demonstrate that the proposed method performed better than baseline methods when applied to this multi-variate time series forecasting problem.},
keywords = {bioinformatics, computational sustainability, dynamic time warping, forecasting, influenza, machine learning, medicine, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
Influenza is a communicable respiratory illness that can cause serious public health hazards. Flu surveillance in New Zealand tracks case counts from various District health boards (DHBs) in the country to monitor the spread of influenza in different geographic locations. Many factors contribute to the spread of the influenza across a geographic region, and it can be challenging to forecast cases in one region without taking into account case numbers in another region. This paper proposes a novel ensemble method called Geographic Ensembles of Observations using Randomised Ensembles of Autoregression Chains (GEO-Reach). GEO-Reach is an ensemble technique that uses a two layer approach to utilise interdependence of historical case counts between geographic regions in New Zealand. This work extends a previously published method by the authors called Randomized Ensembles of Auto-regression chains (Reach). State-of-the-art forecasting models look at studying the spread of the virus. They focus on accurate forecasting of cases for a location using historical case counts for the same location and other data sources based on human behaviour such as movement of people across cities/geographic regions. This new approach is evaluated using Influenza like illness (ILI) case counts in 7 major regions in New Zealand from the years 2015-2019 and compares its performance with other standard methods such as Dante, ARIMA, Autoregression and Random Forests. The results demonstrate that the proposed method performed better than baseline methods when applied to this multi-variate time series forecasting problem.
1.
Poonawala-Lohani, Nooriyan; Riddle, Pat; Adnan, Mehnaz; Wicker, Jörg
A Novel Approach for Time Series Forecasting of Influenza-like Illness Using a Regression Chain Method Proceedings Article
In: Altman, Russ; Dunker, Keith; Hunter, Lawrence; Ritchie, Marylyn; Murray, Tiffany; Klein, Teri (Ed.): Pacific Symposium on Biocomputing, pp. 301-312, 2022.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: computational sustainability, forecasting, influenza, machine learning, time series
@inproceedings{poonawala-lohani2022novel,
title = {A Novel Approach for Time Series Forecasting of Influenza-like Illness Using a Regression Chain Method},
author = {Nooriyan Poonawala-Lohani and Pat Riddle and Mehnaz Adnan and J\"{o}rg Wicker},
editor = {Russ Altman and Keith Dunker and Lawrence Hunter and Marylyn Ritchie and Tiffany Murray and Teri Klein},
url = {https://www.worldscientific.com/doi/abs/10.1142/9789811250477_0028
http://psb.stanford.edu/psb-online/proceedings/psb22/poorawala-lohani.pdf},
doi = {10.1142/9789811250477_0028},
year = {2022},
date = {2022-01-03},
urldate = {2022-01-03},
booktitle = {Pacific Symposium on Biocomputing},
volume = {27},
pages = {301-312},
abstract = {Influenza is a communicable respiratory illness that can cause serious public health hazards. Due to its huge threat to the community, accurate forecasting of Influenza-like-illness (ILI) can diminish the impact of an influenza season by enabling early public health interventions. Current forecasting models are limited in their performance, particularly when using a longer forecasting window. To support better forecasts over a longer forecasting window, we propose to use additional features such as weather data. Commonly used methods to fore-cast ILI, including statistical methods such as ARIMA, limit prediction performance when using additional data sources that might have complex non-linear associations with ILI incidence. This paper proposes a novel time series forecasting method, Randomized Ensembles of Auto-regression chains (Reach). Reach implements an ensemble of random chains for multi-step time series forecasting. This new approach is evaluated on ILI case counts in Auckland, New Zealand from the years 2015-2018 and compared to other standard methods. The results demonstrate that the proposed method performed better than baseline methods when applied to this multi-variate time series forecasting problem.},
keywords = {computational sustainability, forecasting, influenza, machine learning, time series},
pubstate = {published},
tppubtype = {inproceedings}
}
Influenza is a communicable respiratory illness that can cause serious public health hazards. Due to its huge threat to the community, accurate forecasting of Influenza-like-illness (ILI) can diminish the impact of an influenza season by enabling early public health interventions. Current forecasting models are limited in their performance, particularly when using a longer forecasting window. To support better forecasts over a longer forecasting window, we propose to use additional features such as weather data. Commonly used methods to fore-cast ILI, including statistical methods such as ARIMA, limit prediction performance when using additional data sources that might have complex non-linear associations with ILI incidence. This paper proposes a novel time series forecasting method, Randomized Ensembles of Auto-regression chains (Reach). Reach implements an ensemble of random chains for multi-step time series forecasting. This new approach is evaluated on ILI case counts in Auckland, New Zealand from the years 2015-2018 and compared to other standard methods. The results demonstrate that the proposed method performed better than baseline methods when applied to this multi-variate time series forecasting problem.