Chakraborty, Tanujit

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  • Publication
    An ensemble neural network approach to forecast Dengue outbreak based on climatic condition
    (2023)
    Chakraborty, Tanujit 
    ;
    Panja, Madhurima
    ;
    Nadim. Sk Shahid
    ;
    Ghosh, Indrajit
    ;
    Kumar, Uttam
    ;
    Liu, Nan
    Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal's competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.
      14
  • Publication
    PARNN: A Probabilistic Autoregressive Neural Network Framework for Accurate Forecasting
    (2022)
    Hadid, Abdenour 
    ;
    Chakraborty, Tanujit 
    ;
    Panja, Madhurima
    ;
    Kumar, Uttam
    Forecasting time series data represents an emerging field of research in data science and knowledge discovery with vast applications ranging from stock price and energy demand prediction to the early prediction of epidemics. Numerous statistical and machine learning methods have been proposed in the last five decades with the demand for high-quality and reliable forecasts. However, in real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable. Therefore, hybrid solutions are needed to bridge the gap between classical forecasting methods and modern neural network models. In this context, we introduce a Probabilistic AutoRegressive Neural Network (PARNN) model that can handle a wide variety of complex time series data (e.g., nonlinearity, non-seasonal, long-range dependence, and non-stationarity). The proposed PARNN model is built by creating a fusion of an integrated moving average and autoregressive neural network to preserve the explainability, scalability, and "white-boxlike" prediction behavior of the individuals. Sufficient conditions for asymptotic stationarity and geometric ergodicity are obtained by considering the asymptotic behavior of the associated Markov chain. Unlike advanced deep learning tools, the uncertainty quantification of the PARNN model based on prediction intervals is obtained. During computational experiments, PARNN outperforms standard statistical, machine learning, and deep learning models (e.g., Transformers, NBeats, DeepAR, etc.) on a diverse collection of real-world datasets from macroeconomics, tourism, energy, epidemiology, and others for short-term, medium-term, and long-term forecasting. Multiple comparisons with the best method are carried out to showcase the superiority of the proposal in comparison with the state-ofthe-art forecasters over different forecast horizons.
      55  2