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COVID-19 growth prediction using multivariate long short term memory
[article]
2020
arXiv
pre-print
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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
2021
Transboundary and Emerging Diseases
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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]
2021
arXiv
pre-print
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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
2021
Environmental science and pollution research international
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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]
2020
arXiv
pre-print
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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
2021
Nonautonomous Dynamical Systems
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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
2022
Proceedings of The International Conference on Data Science and Official Statistics
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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?
2021
Results in Physics
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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
2021
Cognitive Computation
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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]
2022
arXiv
pre-print
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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
2022
PLoS ONE
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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
2020
2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA)
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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]
2020
arXiv
pre-print
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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
2021
Frontiers in Public Health
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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
2021
Journal of Risk and Financial Management
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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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