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Forecasting Time Series with Multiplicative Trend Exponential Smoothing and LSTM: COVID-19 Case Study

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Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2 (FTC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1289))

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Abstract

In this work, we present an analysis of time series of COVID-19 confirmed cases with Multiplicative Trend Exponential Smoothing (MTES) and Long Short-Term Memory (LSTM). We evaluated the results utilizing COVID-19 confirmed cases data from countries with higher indices as the United States (US), Italy, Spain, and other countries that presumably have stopped the virus, like China, New Zealand, and Australia. Additionally, we used data from a Git repository which is daily updated, when we did the experiments we used data up to April 28th. We used 80% of data to train both models and then, we computed the Root Mean Square Error (RMSE) of test ground true data and predictions. In our experiments, MTES outperformed LSTM, we believe it is caused by a lack of historical data and the particular behavior of each country. To conclude, we performed a forecasting of new COVID-19 confirmed cases using MTES with 10 days ahead.

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Author information

Authors and Affiliations

  1. Universidad Nacional de san Agustín de Arequipa, Arequipa, Peru

    M. A. Machaca Arceda & P. C. Laguna Laura

  2. Universidad la Salle, Arequipa, Peru

    V. E. Machaca Arceda

Authors
  1. M. A. Machaca Arceda
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  2. P. C. Laguna Laura
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  3. V. E. Machaca Arceda
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Corresponding author

Correspondence to V. E. Machaca Arceda .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Arceda, M.A.M., Laura, P.C.L., Arceda, V.E.M. (2021). Forecasting Time Series with Multiplicative Trend Exponential Smoothing and LSTM: COVID-19 Case Study. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2 . FTC 2020. Advances in Intelligent Systems and Computing, vol 1289. Springer, Cham. https://doi.org/10.1007/978-3-030-63089-8_36

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