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COVID-19 growth prediction using multivariate long short term memory [article]

Novanto Yudistira
2020 arXiv   pre-print
To make use of big data in the prediction using deep learning, we use long short-term memory (LSTM) method to learn the correlation of COVID-19 growth over time.  ...  The use of available big data with machine learning is still limited in COVID-19 growth prediction even though the availability of data is abundance.  ...  -19 research.  ... 
arXiv:2005.04809v2 fatcat:jpn6mffatzddplf74rtfieqrhe

Prediction of COVID-19 cases using the weather integrated deep learning approach for India

Kantha Rao Bhimala, Gopal Krishna Patra, Rajashekar Mopuri, Srinivasa Rao Mutheneni
2021 Transboundary and Emerging Diseases  
In the present study, the relationship between weather factor and COVID-19 cases was assessed, and also developed a forecasting model using long short-term memory (LSTM), a deep learning model.  ...  The results showed that the univariate LSTM model was reasonably good for the short-term (1 day lead) forecast of COVID-19 cases (relative error < 20%).  ...  | Long-short term memory (LSTM) model A Long Short-Term Memory (LSTM) network is a kind of Recurrent Neural Network (RNN) that attempts to model time or sequence dependencies (Arora et al., 2020; Hochreiter  ... 
doi:10.1111/tbed.14102 pmid:33837675 pmcid:PMC8250893 fatcat:ghq4nafduzfbfl6cgp7fd5zfie

Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown [article]

Animesh Tiwari, Rishabh Gupta, Rohitash Chandra
2021 arXiv   pre-print
We use a multivariate time series approach that attempts to predict air quality for 10 prediction horizons covering total of 80 hours and provide a long-term (one month ahead) forecast with uncertainties  ...  In this paper, we use novel deep learning methods for short-term (multi-step-ahead) air-quality prediction in selected parts of Delhi, India.  ...  Long Short Term Memory networks (LSTMs) were developed [33] to address limitations in learning long-term dependencies in sequences by canonical RNNs [35, 36] .  ... 
arXiv:2102.10551v1 fatcat:jzwhpkoqpvfzlg5xthvshwzbwu

Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

Ahmed Ben Said, Abdelkarim Erradi, Hussein Ahmed Aly, Abdelmonem Mohamed
2021 Environmental science and pollution research international  
This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series.  ...  AbstractTo assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance.  ...  Chimmula and Zhang (2020) studied the propagation of the virus in Canada using Long Short-Term Memory (LSTM) neural network, known to be efficient with sequential data.  ... 
doi:10.1007/s11356-021-14286-7 pmid:34043172 pmcid:PMC8155803 fatcat:diw7vii2ovdavp5cngrpckkgum

Neural network based country wise risk prediction of COVID-19 [article]

Ratnabali Pal, Arif Ahmed Sekh, Samarjit Kar, Dilip K. Prasad
2020 arXiv   pre-print
Here, we propose a shallow Long short-term memory (LSTM) based neural network to predict the risk category of a country.  ...  The recent worldwide outbreak of the novel corona-virus (COVID-19) opened up new challenges to the research community.  ...  We have proposed to use local trend data and weather data with a shallow Long Short-Term Memory (LSTM) based neural network combined with a feature selection method and fuzzy rules to predict long term  ... 
arXiv:2004.00959v1 fatcat:7seypcpf25dlxofwkolbw4gbfy

Analysis of infectious disease transmission and prediction through SEIQR epidemic model

Swati Tyagi, Shaifu Gupta, Syed Abbas, Krishna Pada Das, Baazaoui Riadh
2021 Nonautonomous Dynamical Systems  
For better insight, in addition to mathematical model, a history based LSTM model is trained to learn temporal patterns in COVID-19 time series and predict future trends.  ...  To validate the model and estimate the parameters to predict the disease spread, we consider the special case for COVID-19 to study the real cases of infected cases from [2] for Russia and India.  ...  Predictions using Long short term memory (LSTM) model Estimation of future COVID-19 cases can be viewed as a time series analysis problem where past trends in the increase of COVID-19 cases are used to  ... 
doi:10.1515/msds-2020-0126 fatcat:jttywdz34vfhjnlfekyuzkyg2a

Analysis of Government Policy in Handling Covid-19 in Indonesia

Atika Kautsar Ilafi, Annisa Nur Fadhilah, Lita Jowanti
2022 Proceedings of The International Conference on Data Science and Official Statistics  
The analysis uses graphs and Pearson Correlation and Long Short-Term Memory (LSTM) method to predict Covid-19 cases and mobility data.  ...  Responding to the Covid-19 pandemic, Google released data from people who access google applications using mobile devices.  ...  The Long short term memory In addition, this study also uses a method, namely Long Short Term Memory (LSTM). LSTM is one of the most common forms of RNN.  ... 
doi:10.34123/icdsos.v2021i1.152 fatcat:ysmsugmycffa5andkjrnjkkmpm

Forecasting of COVID-19 cases using Deep learning models: Is it reliable and practically significant?

Jayanthi Devaraj, Rajvikram Madurai Elavarasan, Rishi Pugazhendhi, G.M. Shafiullah, Sumathi Ganesan, Ajay Kaarthic Jeysree, Irfan Ahmad Khan, Eklas Hossain
2021 Results in Physics  
For long-term forecasting of COVID 19 cases, multivariate LSTM models employed.  ...  Subsequently, the prediction of cumulative confirmed, death and recovered global cases are modelled with Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Stacked Long Short-Term  ...  Acknowledgement The authors acknowledge the support rendered by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, United States, for providing the COVID-19 real-time dataset  ... 
doi:10.1016/j.rinp.2021.103817 pmid:33462560 pmcid:PMC7806459 fatcat:oyycklz67vgyncz5dsddqrpfzm

Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models

Daniela A. Gomez-Cravioto, Ramon E. Diaz-Ramos, Francisco J. Cantu-Ortiz, Hector G. Ceballos
2021 Cognitive Computation  
The results suggest that the logistic growth model fits best the pandemic's behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term  ...  This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico.  ...  Acknowledgements We thank the team from Johns Hopkins University Center for Systems Science and Engineering (CSSE) for their public service by collecting the data of COVID-19 from around the world and  ... 
doi:10.1007/s12559-021-09885-y pmid:34104256 pmcid:PMC8175062 fatcat:gxao7evgzfadvk3ih3td5oelcy

Deep learning via LSTM models for COVID-19 infection forecasting in India [article]

Rohitash Chandra, Ayush Jain, Divyanshu Singh Chauhan
2022 arXiv   pre-print
In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting.  ...  The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture.  ...  The memory cells help in remembering the long-term dependencies in data as shown in Figure 1 .  ... 
arXiv:2101.11881v2 fatcat:2sjby5vwxjcs5kkb4gmkwv2nia

Deep learning via LSTM models for COVID-19 infection forecasting in India

Rohitash Chandra, Ayush Jain, Divyanshu Singh Chauhan, Chi-Hua Chen
2022 PLoS ONE  
In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting.  ...  The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture.  ...  The limitation in training RNNs for long-term dependencies in sequences where data sequences span hundreds or thousands of time-steps [41, 42] have been addressed by long short-term memory networks (  ... 
doi:10.1371/journal.pone.0262708 pmid:35089976 pmcid:PMC8797257 fatcat:glvtukiekrgdzenyfyaxrypw6i

COVID-19 Time Series Forecasting of Daily Cases, Deaths Caused and Recovered Cases using Long Short Term Memory Networks

Suraj Bodapati, Harika Bandarupally, M Trupthi
2020 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)  
To achieve the estimations, we have used the Deep learning model long-shortterm memory network (LSTM).  ...  In this paper the datasets used are obtained from the John Hopkins University's publicly available datasets to develop a state-of-the-art forecasting model of COVID-19 outbreak.  ...  Thus a deep learning model based on long short term memory networks using Pytorch framework can be used to predict the data accurately.  ... 
doi:10.1109/iccca49541.2020.9250863 fatcat:tqtub2z7sfg5ldylpw7zx3a3gu

Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series [article]

Ahmed Ben Said, Abdelkarim Erradi, Hussein Aly, Abdelmonem Mohamed
2020 arXiv   pre-print
Materials and Methods: This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using Bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate  ...  Conclusion: Using data of multiple countries in addition to lockdown measures improve accuracy of the forecast of daily cumulative COVID-19 cases.  ...  [8] studied the propagation of the virus in Canada using Long Short-Term Memory (LSTM) neural network, known to be efficient with sequential data.  ... 
arXiv:2009.12325v1 fatcat:uke7qzkld5f3dkqxobzuesi77q

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

R. Lakshmana Kumar, Firoz Khan, Sadia Din, Shahab S. Band, Amir Mosavi, Ebuka Ibeke
2021 Frontiers in Public Health  
The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning  ...  This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures  ...  (12) adopted a logistic growth curve model for short-term prediction of COVID-19, and SIR models was employed in identifying the highest possible live individuals and peak seasons.  ... 
doi:10.3389/fpubh.2021.744100 pmid:34671588 pmcid:PMC8521000 fatcat:3et3hisqafednfrora7bpmrwka

Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies

Dante Miller, Jong-Min Kim
2021 Journal of Risk and Financial Management  
The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures.  ...  neural networks, Holt's exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks.  ...  This may suggest that the multivariate machine learning model known as long short-term memory networks may be useful in predicting the price returns of cryptocurrencies.  ... 
doi:10.3390/jrfm14100486 fatcat:obru77dfwvf25dp7mp7uf66iau
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