Deep learning

Physics

Shared memory

Pathology

Archaeology

Disease

Coronavirus disease 2019 (COVID-19)

Mathematics

Infectious disease (medical specialty)

Term (time)

Biology

Politics

Operating system

Memory model

Statistics

Mean squared error

Machine learning

Recurrent neural network

Time series

Series (stratigraphy)

Political science

Paleontology

Artificial intelligence

Geography

Representation (politics)

Context (archaeology)

Computer science

Quantum mechanics

Artificial neural network

Medicine

Convolutional neural network

Neural Computing and Applications

The COVID-19 pandemic has disrupted the economy and businesses and impacted all facets of people's lives. It is critical to forecast the number of infected cases to make accurate decisions on the necessary measures to control the outbreak. While deep learning models have proved to be effective in this context, time series augmentation can improve their performance. In this paper, we use time series augmentation techniques to create new time series that take into account the characteristics of the original series, which we then use to generate enough samples to fit deep learning models properly. The proposed method is applied in the context of COVID-19 time series forecasting using three deep learning techniques, (1) the long short-term memory, (2) gated recurrent units, and (3) convolutional neural network. In terms of symmetric mean absolute percentage error and root mean square error measures, the proposed method significantly improves the performance of long short-term memory and convolutional neural networks. Also, the improvement is average for the gated recurrent units. Finally, we present a summary of the top augmentation model as well as a visual representation of the actual and forecasted data for each country.

A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting