Open AccessA Time Series Forecasting with different Visualization Modes of COVID-19 Cases throughout the World
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.34958
Subject(s) - computer science , time series , machine learning , field (mathematics) , artificial intelligence , visualization , covid-19 , series (stratigraphy) , measure (data warehouse) , big data , data mining , data science , infectious disease (medical specialty) , medicine , paleontology , mathematics , disease , pathology , pure mathematics , biology
In real world applications, one of the prosperous field of science is time series forecasting due to its recognition though having some challenges in the development of methods. In medical field, time series forecasting models have been successfully used in various applications to predict progress of the disease, measure the risk dependent on time and the mortality rate. However due to the availability of many techniques which excel in each of a particular scenario, choosing an appropriate model has become challenging. When a huge dataset is considered it is obvious that machine learning is the best way to perform predictive analysis or pattern recognition tasks on the data. Before machine learning can be used, the time series forecasting problems should be reframed into supervised learning problems. The purpose of machine learning in this field is also to tackle the different challenges like data pre-processing, data modelling, training and any other refinement required with respect to the actual data. This paper deals with the predictive analysis and various visualization applications for time series forecasting of COVID- 19 patients throughout the world.