TITLE:
Artificial Neural Networks for COVID-19 Time Series Forecasting
AUTHORS:
Lorena Saliaj, Eugenia Nissi
KEYWORDS:
COVID-19, Time Series Forecasting, ANN, ARIMA
JOURNAL NAME:
Open Journal of Statistics,
Vol.12 No.2,
April
25,
2022
ABSTRACT: Today, COVID-19 pandemic has become the greatest worldwide threat, as it
spreads rapidly among individuals in most countries around the world. This
study concerns the problem of daily prediction of new COVID-19 cases in Italy,
aiming to find the best predictive model for daily infection number in
countries with a large number of confirmed cases. Finding the most accurate
forecasting model would help allocate medical resources, handle the spread of
the pandemic and get more prepared in terms of health care systems. We compare
the forecasting performance of linear and nonlinear forecasting models using
daily COVID-19 data for the period between 22 February 2020 and 10 January
2022. We discuss various forecasting approaches, including an Autoregressive
Integrated Moving Average (ARIMA) model, a Nonlinear Autoregressive Neural
Network (NARNN) model, a TBATS model and Exponential Smoothing on the data
collected from 22 February 2020 to 10 January 2022 and compared their accuracy
using the data collected from 26 March 2020 to 04 April 2020, choosing the
model with the lowest Mean Absolute Percentage Error (MAPE) value. Since the
linear models seem not to easily follow the nonlinear patterns of daily
confirmed COVID-19 cases, Artificial Neural Network (ANN) has been successfully
applied to solve problems of forecasting nonlinear models. The model has been
used for daily prediction of COVID-19 cases for the next 20 days without any
additional intervention. The prediction model can be applied to other countries
struggling with the COVID-19 pandemic and to any possible future pandemics.