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


  • 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



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


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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How to Cite

Mohamad Nor, N. A., Abdul Aziz, A., Wan Shahidan, W. N., Wan Shahidan, W. N., & Muhamad, S. N. N. (2022). Short Term Forecast of COVID-19 cases in Japan Using Time Series Analysis Models. Journal of Computing Research and Innovation, 7(2), 165–174.



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