Anticipating dengue outbreaks using a novel hybrid ARIMA-ARNN model with exogenous variables

TL;DR

This paper introduces a new multivariate hybrid ARIMA-ARNN model with exogenous variables for dengue outbreak prediction, demonstrating improved forecasting accuracy and robustness through theoretical analysis and empirical validation on real data.

Contribution

The paper proposes a novel hybrid ARIMA-ARNN model with exogenous variables, extending existing models for multivariate dengue forecasting with proven stationarity and improved performance.

Findings

The model improves dengue forecast accuracy in some cases.

It closely matches ARIMAX performance in others.

The model is robust and adaptable to additional exogenous variables.

Abstract

Dengue incidence forecasting using hybrid models has been surging in the data rich world. Hybridization of statistical time series forecasting models and machine learning models are explored for dengue forecasting with different degrees of success. In this paper, we propose a multivariate expansion of the hybrid ARIMA-ARNN model. The main motivation is to propose a novel hybridization and apply it to dengue outbreak prediction. The asymptotic stationarity of the proposed model has been established. We check the forecasting capability and robustness of the forecasts through numerical experiments. State-of-the-art forecasting models for multivariate time series data are compared with the proposed model using accuracy metrics. Dengue incidence data from San Juan and Iquitos are utilized along with rainfall as an exogenous variable. Results indicate that the proposed model improves the ARIMAX forecasts in some situations and closely follows it otherwise. The theoretical as well as experimental results reinforce that the proposed model has the potential to act as a candidate for early warning of dengue outbreaks. The proposed model can be readily generalized to incorporate more exogenous variables and also applied to other time series forecasting problems wherever exogenous variable(s) are available.