tetano
Editor, Senior Moderator
Pac Symp Biocomput
. 2022;27:301-312.
A Novel Approach for Time Series Forecasting of Influenza-like Illness Using a Regression Chain Method
Nooriyah Poonawala-Lohani[SUP] 1 [/SUP], Patricia Riddle, Mehnaz Adnan, Jörg Wicker
Affiliations
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. Machine learning models are increasingly being applied in infectious disease modelling, but are limited in their performance, particularly when using a longer forecasting window. This paper proposes a novel time series forecasting method, Randomized Ensembles of Auto-regression chains (Reach). Reach implements an ensemble of random chains for multistep 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 ILI time series forecasting problem.
. 2022;27:301-312.
A Novel Approach for Time Series Forecasting of Influenza-like Illness Using a Regression Chain Method
Nooriyah Poonawala-Lohani[SUP] 1 [/SUP], Patricia Riddle, Mehnaz Adnan, Jörg Wicker
Affiliations
- PMID: 34890158
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. Machine learning models are increasingly being applied in infectious disease modelling, but are limited in their performance, particularly when using a longer forecasting window. This paper proposes a novel time series forecasting method, Randomized Ensembles of Auto-regression chains (Reach). Reach implements an ensemble of random chains for multistep 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 ILI time series forecasting problem.