Dataset Information

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods.

ABSTRACT: Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm - version II is employed to find a set of candidates' weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE) and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model's performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, sMAPE criteria and Diebold-Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modelling developed in this paper is promising and can be used by managers to support decision making.

SUBMITTER: Ribeiro MHDM

PROVIDER: S-EPMC7507988 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods.

Ribeiro Matheus Henrique Dal Molin MHDM Mariani Viviana Cocco VC Coelho Leandro Dos Santos LDS

Journal of biomedical informatics 20200922

Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two, and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual co ...[more]

PMID: 32976990

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Dataset Information

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods.

Publications

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods.

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