FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3205902290> ?p ?o ?g. }

• W3205902290 endingPage "3149" @default.
• W3205902290 startingPage "3135" @default.
• W3205902290 abstract "The COVID-19 pandemic has disrupted the economy and businesses and impacted all facets of people's lives. It is critical to forecast the number of infected cases to make accurate decisions on the necessary measures to control the outbreak. While deep learning models have proved to be effective in this context, time series augmentation can improve their performance. In this paper, we use time series augmentation techniques to create new time series that take into account the characteristics of the original series, which we then use to generate enough samples to fit deep learning models properly. The proposed method is applied in the context of COVID-19 time series forecasting using three deep learning techniques, (1) the long short-term memory, (2) gated recurrent units, and (3) convolutional neural network. In terms of symmetric mean absolute percentage error and root mean square error measures, the proposed method significantly improves the performance of long short-term memory and convolutional neural networks. Also, the improvement is average for the gated recurrent units. Finally, we present a summary of the top augmentation model as well as a visual representation of the actual and forecasted data for each country." @default.
• W3205902290 created "2021-10-25" @default.
• W3205902290 creator A5038143390 @default.
• W3205902290 creator A5057409216 @default.
• W3205902290 creator A5064267652 @default.
• W3205902290 date "2021-10-10" @default.
• W3205902290 modified "2023-10-11" @default.
• W3205902290 title "A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting" @default.
• W3205902290 cites W2003706483 @default.
• W3205902290 cites W2030888282 @default.
• W3205902290 cites W2064675550 @default.
• W3205902290 cites W2067688816 @default.
• W3205902290 cites W2122463941 @default.
• W3205902290 cites W2151536734 @default.
• W3205902290 cites W2157331557 @default.
• W3205902290 cites W2260161590 @default.
• W3205902290 cites W2622826443 @default.
• W3205902290 cites W2913280151 @default.
• W3205902290 cites W2962994110 @default.
• W3205902290 cites W2963122061 @default.
• W3205902290 cites W2967663220 @default.
• W3205902290 cites W2970658101 @default.
• W3205902290 cites W3006028741 @default.
• W3205902290 cites W3006542816 @default.
• W3205902290 cites W3009333463 @default.
• W3205902290 cites W3009906937 @default.
• W3205902290 cites W3013360115 @default.
• W3205902290 cites W3014191625 @default.
• W3205902290 cites W3026389350 @default.
• W3205902290 cites W3030835410 @default.
• W3205902290 cites W3031293910 @default.
• W3205902290 cites W3035592557 @default.
• W3205902290 cites W3035826609 @default.
• W3205902290 cites W3036309913 @default.
• W3205902290 cites W3047031799 @default.
• W3205902290 cites W3049310425 @default.
• W3205902290 cites W3049737176 @default.
• W3205902290 cites W3080544186 @default.
• W3205902290 cites W3099042207 @default.
• W3205902290 cites W3107979244 @default.
• W3205902290 cites W3109289137 @default.
• W3205902290 cites W3115894432 @default.
• W3205902290 cites W3119095567 @default.
• W3205902290 cites W3120105983 @default.
• W3205902290 cites W3130165597 @default.
• W3205902290 cites W3165362162 @default.
• W3205902290 cites W3181655313 @default.
• W3205902290 cites W4232651946 @default.
• W3205902290 cites W4248808158 @default.
• W3205902290 cites W770013183 @default.
• W3205902290 doi "https://doi.org/10.1007/s00521-021-06548-9" @default.
• W3205902290 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8502508" @default.
• W3205902290 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34658536" @default.
• W3205902290 hasPublicationYear "2021" @default.
• W3205902290 type Work @default.
• W3205902290 sameAs 3205902290 @default.
• W3205902290 citedByCount "30" @default.
• W3205902290 countsByYear W32059022902022 @default.
• W3205902290 countsByYear W32059022902023 @default.
• W3205902290 crossrefType "journal-article" @default.
• W3205902290 hasAuthorship W3205902290A5038143390 @default.
• W3205902290 hasAuthorship W3205902290A5057409216 @default.
• W3205902290 hasAuthorship W3205902290A5064267652 @default.
• W3205902290 hasBestOaLocation W32059022901 @default.
• W3205902290 hasConcept C105795698 @default.
• W3205902290 hasConcept C108583219 @default.
• W3205902290 hasConcept C119857082 @default.
• W3205902290 hasConcept C121332964 @default.
• W3205902290 hasConcept C139945424 @default.
• W3205902290 hasConcept C142724271 @default.
• W3205902290 hasConcept C143724316 @default.
• W3205902290 hasConcept C147168706 @default.
• W3205902290 hasConcept C151406439 @default.
• W3205902290 hasConcept C151730666 @default.
• W3205902290 hasConcept C154945302 @default.
• W3205902290 hasConcept C17744445 @default.
• W3205902290 hasConcept C199539241 @default.
• W3205902290 hasConcept C2776359362 @default.
• W3205902290 hasConcept C2779134260 @default.
• W3205902290 hasConcept C2779343474 @default.
• W3205902290 hasConcept C3008058167 @default.
• W3205902290 hasConcept C33923547 @default.
• W3205902290 hasConcept C41008148 @default.
• W3205902290 hasConcept C50644808 @default.
• W3205902290 hasConcept C524204448 @default.
• W3205902290 hasConcept C61797465 @default.
• W3205902290 hasConcept C62520636 @default.
• W3205902290 hasConcept C71924100 @default.
• W3205902290 hasConcept C81363708 @default.
• W3205902290 hasConcept C86803240 @default.
• W3205902290 hasConcept C94625758 @default.
• W3205902290 hasConceptScore W3205902290C105795698 @default.
• W3205902290 hasConceptScore W3205902290C108583219 @default.
• W3205902290 hasConceptScore W3205902290C119857082 @default.
• W3205902290 hasConceptScore W3205902290C121332964 @default.
• W3205902290 hasConceptScore W3205902290C139945424 @default.
• W3205902290 hasConceptScore W3205902290C142724271 @default.
• W3205902290 hasConceptScore W3205902290C143724316 @default.

• next