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A COVID-19 forecasting system using adaptive neuro-fuzzy inference

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  • Ly, Kim Tien

Abstract

This article proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast the number of COVID-19 cases in the United Kingdom. With the combination of artificial neural network and fuzzy logic structure, the model is trained based on collected data. The study examines various factors of ANFIS to come up with an effective time series prediction model. The result indicates that Spain and Italy data can strengthen the predictive power of COVID-19 cases in the UK. It is suggested that the policymakers should adopt Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict contagion effect during the COVID-19 pandemic.

Suggested Citation

  • Ly, Kim Tien, 2021. "A COVID-19 forecasting system using adaptive neuro-fuzzy inference," Finance Research Letters, Elsevier, vol. 41(C).
    • Handle: RePEc:eee:finlet:v:41:y:2021:i:c:s1544612320316585
    DOI: 10.1016/j.frl.2020.101844
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    References listed on IDEAS

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    Cited by:

    1. Muhammad Zubair Mumtaz, 2021. "Predicting Stock Indices Trends using Neuro-fuzzy Systems in COVID-19," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 26(2), pages 1-18, July-Dec.

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    More about this item

    Keywords

    ANFIS; Time series; Forecasting system; Coronavirus; Contagion effect;
    All these keywords.

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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