Utilizing time series for forecasting the development trend of coronavirus: A validation process

Abstract

A time series prediction model was developed to predict the number of confirmed cases from October 2022 to November 2022 based on the number of confirmed cases of New Coronary Pneumonia from January 20, 2021 to September 20, 2022. We will analyze the number of confirmed cases in the Philippines from January 1, 2020 to September 20, 2022 to build a prediction model and make predictions. Among the works of other scholars, it can be shown that time series is an excellent forecasting model, particularly around dates. The study in this work begins with the original data for inference, and each phase of inference is based on objective criteria, such as smooth data analysis utilising ADF detection and ACF graph analysis, and so on. When comparing the performance of algorithms with functions for time series models, hundreds of algorithms are evaluated one by one on the basis of the same data source in order to find the best method. Following the acquisition of the methods, ADF detection and ACF graph analysis are undertaken to validate them, resulting in a closed-loop research. Although the dataset in this study was generated from publicly available data from the Philippines (our data world for coronaviruses), the ARIMA model used to predict data beyond September 20, 2022 exhibited unusually high accuracy. This model was used to compare the performance of several algorithms, each evaluated using the same training data. Finally, the best R2 for the ARIMA model was 92.56% or higher, and iterative optimization of the function produced a predictive model with an R2 of 97.6%. This reveals the potential trajectory of coronaviruses in the Philippines. Finally, the model with the greatest performance is chosen as the prediction model. In actual implementations, several subjective and objective elements, such as the government’s epidemic defence measures, the worldwide pandemic condition, and whether the data source distributes the data in a timely way, might restrict the prediction’s accuracy. Such prediction findings can be used as a foundation for data releases by health agencies.