Short Term Forecast of COVID-19 cases in Japan Using Time Series Analysis Models

Nor Azriani Mohamad Nor; Azlan Abdul Aziz; Wan Nurshazelin Wan Shahidan; Wan Nurshazelin Wan Shahidan; Siti Nor Nadrah Muhamad

doi:10.24191/jcrinn.v7i2.301

Short Term Forecast of COVID-19 cases in Japan Using Time Series Analysis Models

Authors

Nor Azriani Mohamad Nor Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis Branch
Azlan Abdul Aziz Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis Branch
Wan Nurshazelin Wan Shahidan Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis Branch
Wan Nurshazelin Wan Shahidan Universiti Teknologi MARA, Cawangan Perlis
Siti Nor Nadrah Muhamad Universiti Teknologi MARA, Cawangan Perlis

DOI:

https://doi.org/10.24191/jcrinn.v7i2.301

Keywords:

COVID-19, Forecasting, Time-series Analysis, ARIMA, State space Model, ETS Model

Abstract

The new strain of coronavirus (COVID-19) was found to have started in Wuhan, China in late December 2019. The virus has spread to countries all over the world including Japan. The World Health Organization (WHO) declared COVID-19 as a pandemic on 11 March 2020 due to the increasing number of confirmed cases and deaths daily. The COVID-19 outbreak has impacted the nation of Japan adversely and the number of confirmed cases in Japan continues to increase day by day. On 7 April 2020, Japan declared a state of emergency to prevent the pandemic from worsening. This study is conducted to forecast new daily confirmed cases of COVID-19 in Japan over a short-term period. Four univariate time series models were applied: the Naïve Model, Mean Model, Autoregressive Integrated Moving Average (ARIMA) Model and Exponential State Space Model. This study analyses daily data from 22 January to 10 April 2020 collected from the Our World in Data website. The prediction involves five phases of data analysis and five different partitions of estimation and evaluation parts in every model to ensure the accuracy of forecast values. R and R Studio software were used in this study to analyze the data. The results reveal that Naïve model with 99 percent of estimation part and 1 percent evaluation part produces the lowest value of error measures for Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE).

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