IDEAS home Printed from https://ideas.repec.org/a/tec/journl/v16y2021i1p618-630.html
   My bibliography  Save this article

Forecasting Method Using the Minitab Program

Author

Listed:
  • Sunarsan Sitohang

    (Putera Batam University of Informatic Enginering, Batam, Indonesia)

  • Very Karnadi

    (Putera Batam University of Informatic Enginering, Batam, Indonesia)

Abstract

The Corona Virus 2019 (COVID-19) pandemic has yet to subside. This epidemic has spread to almost all countries in the world. This pandemic has resulted in the decrease of the activities of people and the economy. The COVID-19 pandemic itself spread in Indonesia on March 2, 2020, to be precise. Two people tested positive for COVID-19, and they were referred to as case 1 and 2. After the detection of the COVID-19 pandemic in Indonesia, Indonesia experienced additional positive cases of COVID-19 every day. This study aims - to build a model to predict the development of COVID-19 cases based on time series data and the number of COVID-19 sufferers, using seven forecasting methods, namely, Exponential Smoothing, Exponential Smoothing with Trend, Linear Regression/Least Squares, Moving Average, Trend Analysis (Regression Over Time), Additive Decomposition (Seasonal), and Multiplicative Decomposition (Seasonal), with the Minitab program, including in Indonesia. The results showed that the Exponential Smoothing with Trend method, has mean absolute deviation (MAD) of 220.6and mean squared error (MSE) of 69,994.2; the forecast resulted in 177,621 number of COVID-19 cases, which is predicted to occur on September 1, 2020.

Suggested Citation

  • Sunarsan Sitohang & Very Karnadi, 2021. "Forecasting Method Using the Minitab Program," Technium Social Sciences Journal, Technium Science, vol. 16(1), pages 618-630, February.
    • Handle: RePEc:tec:journl:v:16:y:2021:i:1:p:618-630
    as

    Download full text from publisher

    File URL: https://techniumscience.com/index.php/socialsciences/article/view/2376/1001
    Download Restriction: no
    File URL: https://techniumscience.com/index.php/socialsciences/article/view/2376
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammed Hussein Jabardi, 2022. "Forecasting Weekly COVID-19 Infection and Death Cases in Iraq Using an ARIMA Model," Technium, Technium Science, vol. 4(1), pages 64-75.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    3. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    4. Yunhan Huang & Quanyan Zhu, 2022. "Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review," Dynamic Games and Applications, Springer, vol. 12(1), pages 7-48, March.
    5. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    6. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    7. Crokidakis, Nuno, 2020. "COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    8. Bowen Long & Fangya Tan & Mark Newman, 2023. "Forecasting the Monkeypox Outbreak Using ARIMA, Prophet, NeuralProphet, and LSTM Models in the United States," Forecasting, MDPI, vol. 5(1), pages 1-11, January.
    9. Prasanth, Sikakollu & Singh, Uttam & Kumar, Arun & Tikkiwal, Vinay Anand & Chong, Peter H.J., 2021. "Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    10. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    11. Kun Zhang & Xing Huo & Kun Shao, 2023. "Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network," Mathematics, MDPI, vol. 11(9), pages 1-16, April.
    12. Shalini Shekhawat & Akash Saxena & Ramadan A. Zeineldin & Ali Wagdy Mohamed, 2023. "Prediction of Infectious Disease to Reduce the Computation Stress on Medical and Health Care Facilitators," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    13. Khan, Firdos & Saeed, Alia & Ali, Shaukat, 2020. "Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    15. Yi-Tui Chen & Yung-Feng Yen & Shih-Heng Yu & Emily Chia-Yu Su, 2020. "An Examination on the Transmission of COVID-19 and the Effect of Response Strategies: A Comparative Analysis," IJERPH, MDPI, vol. 17(16), pages 1-14, August.
    16. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    17. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    18. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    19. Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    20. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).

    More about this item

    Keywords

    forecasting; Minitab; seven method; COVID-19;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tec:journl:v:16:y:2021:i:1:p:618-630. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tasente Tanase (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.