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

doi:10.1007/978-3-030-63089-8_36

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 congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

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 original	Inglés
Título de la publicación alojada	Proceedings of the Future Technologies Conference, FTC 2020, Volume 2
Editores	Kohei Arai, Supriya Kapoor, Rahul Bhatia
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	568-582
Número de páginas	15
ISBN (versión impresa)	9783030630881
DOI	https://doi.org/10.1007/978-3-030-63089-8_36
Estado	Publicada - 2021
Publicado de forma externa	Sí
Evento	Future Technologies Conference, FTC 2020 - San Francisco, Estados Unidos Duración: 5 nov. 2020 → 6 nov. 2020

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1289
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	Future Technologies Conference, FTC 2020
País/Territorio	Estados Unidos
Ciudad	San Francisco
Período	5/11/20 → 6/11/20

Nota bibliográfica

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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10.1007/978-3-030-63089-8_36

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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. En K. Arai, S. Kapoor, & R. Bhatia (Eds.), Proceedings of the Future Technologies Conference, FTC 2020, Volume 2 (pp. 568-582). (Advances in Intelligent Systems and Computing; Vol. 1289). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63089-8_36

Arceda, M. A.Machaca ; Laura, P. C.Laguna ; Arceda, V. E.Machaca. / Forecasting Time Series with Multiplicative Trend Exponential Smoothing and LSTM : COVID-19 Case Study. Proceedings of the Future Technologies Conference, FTC 2020, Volume 2. editor / Kohei Arai ; Supriya Kapoor ; Rahul Bhatia. Springer Science and Business Media Deutschland GmbH, 2021. pp. 568-582 (Advances in Intelligent Systems and Computing).

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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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Arceda, MAM, Laura, PCL & Arceda, VEM 2021, Forecasting Time Series with Multiplicative Trend Exponential Smoothing and LSTM: COVID-19 Case Study. En K Arai, S Kapoor & R Bhatia (eds.), Proceedings of the Future Technologies Conference, FTC 2020, Volume 2. Advances in Intelligent Systems and Computing, vol. 1289, Springer Science and Business Media Deutschland GmbH, pp. 568-582, Future Technologies Conference, FTC 2020, San Francisco, Estados Unidos, 5/11/20. https://doi.org/10.1007/978-3-030-63089-8_36

Forecasting Time Series with Multiplicative Trend Exponential Smoothing and LSTM : COVID-19 Case Study. / Arceda, M. A.Machaca; Laura, P. C.Laguna; Arceda, V. E.Machaca.

Proceedings of the Future Technologies Conference, FTC 2020, Volume 2. ed. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Springer Science and Business Media Deutschland GmbH, 2021. p. 568-582 (Advances in Intelligent Systems and Computing; Vol. 1289).

Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AU - Laura, P. C.Laguna

AU - Arceda, V. E.Machaca

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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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Arceda MAM, Laura PCL, Arceda VEM. Forecasting Time Series with Multiplicative Trend Exponential Smoothing and LSTM: COVID-19 Case Study. En Arai K, Kapoor S, Bhatia R, editores, Proceedings of the Future Technologies Conference, FTC 2020, Volume 2. Springer Science and Business Media Deutschland GmbH. 2021. p. 568-582. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-63089-8_36

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

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