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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


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.

Idioma originalInglés
Título de la publicación alojadaProceedings of the Future Technologies Conference, FTC 2020, Volume 2
EditoresKohei Arai, Supriya Kapoor, Rahul Bhatia
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas15
ISBN (versión impresa)9783030630881
EstadoPublicada - 2021
Publicado de forma externa
EventoFuture Technologies Conference, FTC 2020 - San Francisco, Estados Unidos
Duración: 5 nov. 20206 nov. 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing
ISSN (versión impresa)2194-5357
ISSN (versión digital)2194-5365


ConferenciaFuture Technologies Conference, FTC 2020
País/TerritorioEstados Unidos
CiudadSan Francisco

Nota bibliográfica

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


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