Long Short-Term Memory Prediction for COVID19 Time Series
Entire world has been dealing with the number of new Coronavirus 2 or COVID-19 cases. The spread of this severe acute respiratory syndrome has produced many concerns worldwide. Having data related to coronavirus available for tests, novel models for forecasting the number of new cases can be develop...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Telecommunications Society, Academic Mind
2021-12-01
|
| Series: | Telfor Journal |
| Subjects: | |
| Online Access: |
http://journal.telfor.rs/Published/Vol13No2/Vol13No2_A4.pdf
|
| _version_ | 1811186293806202880 |
|---|---|
| author | M. S. Milivojević A. Gavrovska |
| author_facet | M. S. Milivojević A. Gavrovska |
| author_sort | M. S. Milivojević |
| collection | DOAJ |
| description | Entire world has been dealing with the number of new Coronavirus 2 or COVID-19 cases. The spread of this severe acute respiratory syndrome has produced many concerns worldwide. Having data related to coronavirus available for tests, novel models for forecasting the number of new cases can be developed. In this paper, a long short-term memory (LSTM) based methodology is applied for such prediction. Here, experimental analysis is performed with the parameters, such as the number of layers and units of the network. The root mean squared error is calculated for data corresponding to the Republic of Serbia, as well as per different continents. The results show that LSTM model can be useful for further analysis and time series prediction. |
| first_indexed | 2024-04-11T13:44:16Z |
| format | Article |
| id | doaj.art-8d097b59d89a4a328551baaa4b2d9a19 |
| institution | Directory Open Access Journal |
| issn | 1821-3251 |
| language | English |
| last_indexed | 2024-04-11T13:44:16Z |
| publishDate | 2021-12-01 |
| publisher | Telecommunications Society, Academic Mind |
| record_format | Article |
| series | Telfor Journal |
| spelling | doaj.art-8d097b59d89a4a328551baaa4b2d9a192022-12-22T04:21:09ZengTelecommunications Society, Academic MindTelfor Journal1821-32512021-12-01132818610.5937/telfor2102081MLong Short-Term Memory Prediction for COVID19 Time SeriesM. S. MilivojevićA. GavrovskaEntire world has been dealing with the number of new Coronavirus 2 or COVID-19 cases. The spread of this severe acute respiratory syndrome has produced many concerns worldwide. Having data related to coronavirus available for tests, novel models for forecasting the number of new cases can be developed. In this paper, a long short-term memory (LSTM) based methodology is applied for such prediction. Here, experimental analysis is performed with the parameters, such as the number of layers and units of the network. The root mean squared error is calculated for data corresponding to the Republic of Serbia, as well as per different continents. The results show that LSTM model can be useful for further analysis and time series prediction. http://journal.telfor.rs/Published/Vol13No2/Vol13No2_A4.pdf predictionneural networklstmacute respiratory syndromecovid-19root mean squared error |
| spellingShingle | M. S. Milivojević A. Gavrovska Long Short-Term Memory Prediction for COVID19 Time Series Telfor Journal prediction neural network lstm acute respiratory syndrome covid-19 root mean squared error |
| title | Long Short-Term Memory Prediction for COVID19 Time Series |
| title_full | Long Short-Term Memory Prediction for COVID19 Time Series |
| title_fullStr | Long Short-Term Memory Prediction for COVID19 Time Series |
| title_full_unstemmed | Long Short-Term Memory Prediction for COVID19 Time Series |
| title_short | Long Short-Term Memory Prediction for COVID19 Time Series |
| title_sort | long short term memory prediction for covid19 time series |
| topic | prediction neural network lstm acute respiratory syndrome covid-19 root mean squared error |
| url |
http://journal.telfor.rs/Published/Vol13No2/Vol13No2_A4.pdf
|
| work_keys_str_mv | AT msmilivojevic longshorttermmemorypredictionforcovid19timeseries AT agavrovska longshorttermmemorypredictionforcovid19timeseries |