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

M. A.Machaca Arceda, P. C.Laguna Laura, V. E.Machaca Arceda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationProceedings of the Future Technologies Conference, FTC 2020, Volume 2
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030630881
StatePublished - 2021
Externally publishedYes
EventFuture Technologies Conference, FTC 2020 - San Francisco, United States
Duration: 5 Nov 20206 Nov 2020

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceFuture Technologies Conference, FTC 2020
Country/TerritoryUnited States
CitySan Francisco

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • COVID-19
  • Forecasting
  • LSTM
  • MTES
  • Time series


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