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      Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic

      research-article
      * ,
      Chaos, Solitons, and Fractals
      Elsevier Ltd.
      Fractal dimension, Fuzzy logic, Forecasting, Time series, COVID-19

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          • A hybrid intelligent approach for forecasting COVID-19 time series combining fractal theory and fuzzy logic is presented.

          • The fractal dimension is used to measure the complexity of the dynamics in the time series of the countries in the world.

          • Fuzzy Logic is used to represent the uncertainty in the process of making a forecast.

          • The hybrid approach consists on a fuzzy model formed by a set of fuzzy rules that uses as input values the linear and nonlinear fractal dimensions of the time series.

          • The outputs are the forecast for the countries based on the COVID-19 time series of confirmed cases and deaths.

          Abstract

          We describe in this paper a hybrid intelligent approach for forecasting COVID-19 time series combining fractal theory and fuzzy logic. The mathematical concept of the fractal dimension is used to measure the complexity of the dynamics in the time series of the countries in the world. Fuzzy Logic is used to represent the uncertainty in the process of making a forecast. The hybrid approach consists on a fuzzy model formed by a set of fuzzy rules that use as input values the linear and nonlinear fractal dimensions of the time series and as outputs the forecast for the countries based on the COVID-19 time series of confirmed cases and deaths. The main contribution is the proposed hybrid approach combining the fractal dimension and fuzzy logic for enabling an efficient and accurate forecasting of COVID-19 time series. Publicly available data sets of 10 countries in the world have been used to build the fuzzy model with time series in a fixed period. After that, other periods of time were used to verify the effectiveness of the proposed approach for the forecasted values of the 10 countries. Forecasting windows of 10 and 30 days ahead were used to test the proposed approach. Forecasting average accuracy is 98%, which can be considered good considering the complexity of the COVID problem. The proposed approach can help people in charge of decision making to fight the pandemic can use the information of a short window to decide immediate actions and also the longer window (like 30 days) can be beneficial in long term decisions.

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          Most cited references26

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          • Article: found

          World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)

          An unprecedented outbreak of pneumonia of unknown aetiology in Wuhan City, Hubei province in China emerged in December 2019. A novel coronavirus was identified as the causative agent and was subsequently termed COVID-19 by the World Health Organization (WHO). Considered a relative of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), COVID-19 is caused by a betacoronavirus named SARS-CoV-2 that affects the lower respiratory tract and manifests as pneumonia in humans. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 continues to rise, with 90,870 laboratory-confirmed cases and over 3,000 deaths worldwide. In response to this global outbreak, we summarise the current state of knowledge surrounding COVID-19.
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            COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses

            Graphical abstract
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              Analysis and forecast of COVID-19 spreading in China, Italy and France

              Highlights • Epidemic spreading • COVID19 • SIR model • Recursive relations and non linear fitting
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                Author and article information

                Contributors
                Journal
                Chaos Solitons Fractals
                Chaos Solitons Fractals
                Chaos, Solitons, and Fractals
                Elsevier Ltd.
                0960-0779
                0960-0779
                24 August 2020
                November 2020
                24 August 2020
                : 140
                : 110242
                Affiliations
                [0001]Tijuana Institute of Technology, Tijuana, Mexico
                Author notes
                Article
                S0960-0779(20)30638-X 110242
                10.1016/j.chaos.2020.110242
                7444908
                ebf1623f-5073-4468-8a84-c44f1ab982bc
                © 2020 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 10 June 2020
                : 10 August 2020
                : 22 August 2020
                Categories
                Article

                fractal dimension,fuzzy logic,forecasting,time series,covid-19

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