scholarly journals A Time Series Forecasting with different Visualization Modes of COVID-19 Cases throughout the World

2021 ◽  
Vol 9 (VI) ◽  
pp. 505-510
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Forecasting ◽  
Learning Problems ◽  
Predictive Analysis ◽  
Data Modelling ◽  
Medical Field ◽  
Processing Data ◽  
The World ◽  
Real World Applications

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.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

An intelligent hybridization of ARIMA with machine learning models for time series forecasting

2019 ◽  
Vol 175 ◽  
pp. 72-86 ◽  
Author(s):  
Domingos S. de O. Santos Júnior ◽  
João F.L. de Oliveira ◽  
Paulo S.G. de Mattos Neto
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Forecasting ◽  
Learning Models ◽  
Machine Learning Models

An Empirical Comparison of Machine Learning Models for Time Series Forecasting

2010 ◽  
Vol 29 (5-6) ◽  
pp. 594-621 ◽  
Author(s):  
Nesreen K. Ahmed ◽  
Amir F. Atiya ◽  
Neamat El Gayar ◽  
Hisham El-Shishiny
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Forecasting ◽  
Learning Models ◽  
Empirical Comparison ◽  
Machine Learning Models

scholarly journals An Effective Prediction on COVID-19 Prevalence for India and Japan using Fbprophet Model

2021 ◽  
pp. 16-28
Author(s):  
Md. Mehedi Rahman Rana ◽  
Farjana Rahman ◽  
Jabed Al Faysal ◽  
Md. Anisur Rahman
Keyword(s):  
Time Series ◽  
Infection Rate ◽  
Satisfactory Result ◽  
Death Rate ◽  
Large Scale ◽  
Time Series Forecasting ◽  
Economic Life ◽  
Substantial Influence ◽  
The World

Coronavirus has become a significant concern for the whole world. It has had a substantial influence on our social and economic life. The infection rate is rapidly increasing at every moment throughout the world. At present, predicting coronavirus has become one of the challenging issues for us. As the pace of COVID-19 detection increases, so does the death rate. This research predicts the number of coronavirus detection and deaths using Fbprophet, a tool designed to assist in performing time series forecasting at a large scale. Two major affected countries, India and Japan, have been taken into consideration in our approach.  Using the prophet model, a prediction is performed on the number of total cases, new cases, total deaths and new deaths. This model works considerably well, and it has given a satisfactory result that may help the authority in taking early and appropriate decisions depending on the predicted COVID situation.

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