Matches in SemOpenAlex for { <https://semopenalex.org/work/W2899693449> ?p ?o ?g. }
- W2899693449 endingPage "310" @default.
- W2899693449 startingPage "283" @default.
- W2899693449 abstract "In this article we introduce a new stability-based dynamic Bayesian network method for dynamic systems represented by their time series. Based on the Grow Shrink algorithm and the stability of the network through time, new variables and arcs could be added to the network in order to generate missing data or predict future values. The concept of stability in the network is maintained through a stability matrix which contains learned values that indicate the strength of dependencies between variables along the time. Moreover, we present the application of the proposed method to deal with the problem of prediction in a real-life air quality case study, in which we try to predict the level of Carbon monoxide in the air, comparing between the results obtained using the proposed method and those obtained using the Vector Autoregression model." @default.
- W2899693449 created "2018-11-16" @default.
- W2899693449 creator A5022213286 @default.
- W2899693449 creator A5063534623 @default.
- W2899693449 creator A5064625587 @default.
- W2899693449 creator A5080925118 @default.
- W2899693449 date "2019-01-01" @default.
- W2899693449 modified "2023-10-06" @default.
- W2899693449 title "Stability-based Dynamic Bayesian Network method for dynamic data mining" @default.
- W2899693449 cites W1517993545 @default.
- W2899693449 cites W1588163064 @default.
- W2899693449 cites W1760772398 @default.
- W2899693449 cites W1972619302 @default.
- W2899693449 cites W1973807737 @default.
- W2899693449 cites W1973943669 @default.
- W2899693449 cites W1975379468 @default.
- W2899693449 cites W1977060302 @default.
- W2899693449 cites W1984255960 @default.
- W2899693449 cites W1989130706 @default.
- W2899693449 cites W1990376219 @default.
- W2899693449 cites W2006877085 @default.
- W2899693449 cites W2022637272 @default.
- W2899693449 cites W2027334863 @default.
- W2899693449 cites W2040731319 @default.
- W2899693449 cites W2041702814 @default.
- W2899693449 cites W2041706261 @default.
- W2899693449 cites W2049478981 @default.
- W2899693449 cites W2070986256 @default.
- W2899693449 cites W2080180737 @default.
- W2899693449 cites W2084095064 @default.
- W2899693449 cites W2089805406 @default.
- W2899693449 cites W2110297764 @default.
- W2899693449 cites W2113273949 @default.
- W2899693449 cites W2117014758 @default.
- W2899693449 cites W2117558025 @default.
- W2899693449 cites W2121146753 @default.
- W2899693449 cites W2126831543 @default.
- W2899693449 cites W2129046665 @default.
- W2899693449 cites W2137788236 @default.
- W2899693449 cites W2142635246 @default.
- W2899693449 cites W2148555761 @default.
- W2899693449 cites W2161632986 @default.
- W2899693449 cites W2163166770 @default.
- W2899693449 cites W2165190832 @default.
- W2899693449 cites W2168175751 @default.
- W2899693449 cites W2175143722 @default.
- W2899693449 cites W2301106258 @default.
- W2899693449 cites W2301884459 @default.
- W2899693449 cites W2963269335 @default.
- W2899693449 doi "https://doi.org/10.1016/j.engappai.2018.09.016" @default.
- W2899693449 hasPublicationYear "2019" @default.
- W2899693449 type Work @default.
- W2899693449 sameAs 2899693449 @default.
- W2899693449 citedByCount "10" @default.
- W2899693449 countsByYear W28996934492019 @default.
- W2899693449 countsByYear W28996934492021 @default.
- W2899693449 countsByYear W28996934492022 @default.
- W2899693449 countsByYear W28996934492023 @default.
- W2899693449 crossrefType "journal-article" @default.
- W2899693449 hasAuthorship W2899693449A5022213286 @default.
- W2899693449 hasAuthorship W2899693449A5063534623 @default.
- W2899693449 hasAuthorship W2899693449A5064625587 @default.
- W2899693449 hasAuthorship W2899693449A5080925118 @default.
- W2899693449 hasConcept C112972136 @default.
- W2899693449 hasConcept C11413529 @default.
- W2899693449 hasConcept C119857082 @default.
- W2899693449 hasConcept C124101348 @default.
- W2899693449 hasConcept C13540734 @default.
- W2899693449 hasConcept C151406439 @default.
- W2899693449 hasConcept C154945302 @default.
- W2899693449 hasConcept C197298091 @default.
- W2899693449 hasConcept C199360897 @default.
- W2899693449 hasConcept C31258907 @default.
- W2899693449 hasConcept C33724603 @default.
- W2899693449 hasConcept C41008148 @default.
- W2899693449 hasConcept C82142266 @default.
- W2899693449 hasConceptScore W2899693449C112972136 @default.
- W2899693449 hasConceptScore W2899693449C11413529 @default.
- W2899693449 hasConceptScore W2899693449C119857082 @default.
- W2899693449 hasConceptScore W2899693449C124101348 @default.
- W2899693449 hasConceptScore W2899693449C13540734 @default.
- W2899693449 hasConceptScore W2899693449C151406439 @default.
- W2899693449 hasConceptScore W2899693449C154945302 @default.
- W2899693449 hasConceptScore W2899693449C197298091 @default.
- W2899693449 hasConceptScore W2899693449C199360897 @default.
- W2899693449 hasConceptScore W2899693449C31258907 @default.
- W2899693449 hasConceptScore W2899693449C33724603 @default.
- W2899693449 hasConceptScore W2899693449C41008148 @default.
- W2899693449 hasConceptScore W2899693449C82142266 @default.
- W2899693449 hasLocation W28996934491 @default.
- W2899693449 hasOpenAccess W2899693449 @default.
- W2899693449 hasPrimaryLocation W28996934491 @default.
- W2899693449 hasRelatedWork W1964038743 @default.
- W2899693449 hasRelatedWork W2061193177 @default.
- W2899693449 hasRelatedWork W2140550119 @default.
- W2899693449 hasRelatedWork W2322656384 @default.
- W2899693449 hasRelatedWork W2544242547 @default.
- W2899693449 hasRelatedWork W2785561748 @default.